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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
126e876898a0e0bf05c46508b50bf5ecd64d7aa0 | 877edd932f97480f80550c6ff2f68b22e4099bd0 | /ex001 deixando tudo pronto.py | ee2787c636369016eb9a9ce663f543248a723e82 | [] | no_license | lacerda92/exercicios-python | fcb57bb09d96a5b6cc6419a08daf56d3370aad3e | 6c07e1fcd19a4ad1b2d57f0254acf8802ea4002c | refs/heads/main | 2023-03-19T03:40:14.896581 | 2021-03-12T09:39:19 | 2021-03-12T09:39:19 | 345,802,634 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 255 | py | # This is a sample Python script.
# Press Shift+F10 to execute it or replace it with your code.
# Press Double Shift to search everywhere for classes, files, tool windows, actio
print('Olá, mundo!')
msg='Olá, mundo de merda!'
print(msg)
| [
"noreply@github.com"
] | noreply@github.com |
b7856a073dc64c9ca94c71ada8496a578bca305a | e71ad02e8705b65933bda8057568637c6c81dd30 | /migrations/versions/a4761f431940_initial_migration.py | d7a51ef921569b331e677e92f4b5418a73533ff5 | [
"MIT"
] | permissive | mukasine/pitch | 5b3cf0e25628fb96f416b855f1e6c7d677129077 | 85daae8473c30e69badac20babdd2067ecc5af4c | refs/heads/master | 2020-04-25T22:24:28.529612 | 2019-03-01T15:35:05 | 2019-03-01T15:35:05 | 173,110,464 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 669 | py | """Initial Migration
Revision ID: a4761f431940
Revises: 80dba82291bb
Create Date: 2019-02-28 17:16:10.586139
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = 'a4761f431940'
down_revision = '80dba82291bb'
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.add_column('users', sa.Column('firstname', sa.String(length=255), nullable=True))
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.drop_column('users', 'firstname')
# ### end Alembic commands ###
| [
"mclaremukasine@gmail.com"
] | mclaremukasine@gmail.com |
fa7775ca7aecd1ea0f1f6b49c41e794eab571f77 | afff816bb9a54817722a76c4fafb0aeabaf5624c | /Practical5/Powers of 2.py | 5549dbc9548f3dd14875d20793d24c152b3ef567 | [] | no_license | Yaqi-SU/IBI1_2019-20 | c4e5584a082953cea9783cb81da159decff0871e | 54ec58196c4a35a53908c4e2fd2f416c508e7cd3 | refs/heads/master | 2021-02-14T10:20:18.133592 | 2020-05-14T11:57:12 | 2020-05-14T11:57:12 | 244,796,477 | 0 | 0 | null | 2020-03-04T03:14:41 | 2020-03-04T03:14:40 | null | UTF-8 | Python | false | false | 589 | py | # -*- coding: utf-8 -*-
"""
Created on Wed Mar 11 20:21:25 2020
@author: suyaqi
"""
#Enter a number
x=input('Enter a number:')
t=eval(x)
#Convert the decimal number into binary
a=bin(t)
#Discard 0b
b=a[2:]
#Divide b into a list
c=list(b)
# Set an empty string to store the powers of 2
n=''
#Loop through all numbers in list b
for i in range(len(c)):
# If c[i]==1: n=n+2**(len(c)-i-1),i=i+1
c[i]=int(c[i])
if c[i]==1:
n+=str(2)+'**'+str(len(c)-i-1)+'+'
i+=1
#if not: continue
else:
continue
print(eval(x),'is',n[:-1])
| [
"yaqi.19@intl.zju.edu.cn"
] | yaqi.19@intl.zju.edu.cn |
1d1763810e274bc281dd01f65f57cebd9b880b7a | 0944f6b2d222ed28e0d93686b3e9ac5fcb81e849 | /bindings/python/src/cloudsmith_api/models/formats_distributions.py | 4ebf12d0446ed22ec4ee74d877e2ab83a07cacf2 | [
"Apache-2.0",
"LicenseRef-scancode-warranty-disclaimer"
] | permissive | ryanwilsonperkin/cloudsmith-api | 72ad3aa0b737dbe4670a2e2e1224469d0883a856 | 73533364a0880186ed958ce71f38613be6a756c6 | refs/heads/master | 2023-08-14T17:47:55.800669 | 2021-05-17T13:51:49 | 2021-05-17T13:51:49 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,984 | py | # coding: utf-8
"""
Cloudsmith API
The API to the Cloudsmith Service
OpenAPI spec version: v1
Contact: support@cloudsmith.io
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from pprint import pformat
from six import iteritems
import re
class FormatsDistributions(object):
"""
NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
swagger_types = {
'name': 'str',
'self_url': 'str',
'slug': 'str',
'variants': 'str'
}
attribute_map = {
'name': 'name',
'self_url': 'self_url',
'slug': 'slug',
'variants': 'variants'
}
def __init__(self, name=None, self_url=None, slug=None, variants=None):
"""
FormatsDistributions - a model defined in Swagger
"""
self._name = None
self._self_url = None
self._slug = None
self._variants = None
if name is not None:
self.name = name
if self_url is not None:
self.self_url = self_url
if slug is not None:
self.slug = slug
if variants is not None:
self.variants = variants
@property
def name(self):
"""
Gets the name of this FormatsDistributions.
:return: The name of this FormatsDistributions.
:rtype: str
"""
return self._name
@name.setter
def name(self, name):
"""
Sets the name of this FormatsDistributions.
:param name: The name of this FormatsDistributions.
:type: str
"""
self._name = name
@property
def self_url(self):
"""
Gets the self_url of this FormatsDistributions.
:return: The self_url of this FormatsDistributions.
:rtype: str
"""
return self._self_url
@self_url.setter
def self_url(self, self_url):
"""
Sets the self_url of this FormatsDistributions.
:param self_url: The self_url of this FormatsDistributions.
:type: str
"""
self._self_url = self_url
@property
def slug(self):
"""
Gets the slug of this FormatsDistributions.
The slug identifier for this distribution
:return: The slug of this FormatsDistributions.
:rtype: str
"""
return self._slug
@slug.setter
def slug(self, slug):
"""
Sets the slug of this FormatsDistributions.
The slug identifier for this distribution
:param slug: The slug of this FormatsDistributions.
:type: str
"""
self._slug = slug
@property
def variants(self):
"""
Gets the variants of this FormatsDistributions.
:return: The variants of this FormatsDistributions.
:rtype: str
"""
return self._variants
@variants.setter
def variants(self, variants):
"""
Sets the variants of this FormatsDistributions.
:param variants: The variants of this FormatsDistributions.
:type: str
"""
self._variants = variants
def to_dict(self):
"""
Returns the model properties as a dict
"""
result = {}
for attr, _ in iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
return result
def to_str(self):
"""
Returns the string representation of the model
"""
return pformat(self.to_dict())
def __repr__(self):
"""
For `print` and `pprint`
"""
return self.to_str()
def __eq__(self, other):
"""
Returns true if both objects are equal
"""
if not isinstance(other, FormatsDistributions):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""
Returns true if both objects are not equal
"""
return not self == other
| [
"lskillen@cloudsmith.io"
] | lskillen@cloudsmith.io |
171eaf38d54a5fe7dcf2a23a97cf6c845c890e8d | cff5ac961d717059caf25dc4247ddcc958f27d24 | /WRAPPERS/corrmat_from_regionalmeasures.py | 1be8e37d8ef4eb4850aaa0156d84101d217d6c98 | [
"MIT"
] | permissive | repropaper/NSPN_WhitakerVertes_PNAS2016 | fac4a9bb72e92db2d38b5c41e431e998c8114030 | 5c9c46caf91768d4cadec2b24078b640f05d3d76 | refs/heads/reprobranch | 2020-03-19T00:17:24.346727 | 2017-02-23T08:24:50 | 2017-02-23T08:24:50 | 135,469,739 | 0 | 1 | MIT | 2018-05-30T17:08:00 | 2018-05-30T16:26:32 | OpenEdge ABL | UTF-8 | Python | false | false | 6,687 | py | #!/usr/bin/env python
#=============================================================================
# Created by Kirstie Whitaker
# at Hot Numbers coffee shop on Trumpington Road in Cambridge, September 2016
# Contact: kw401@cam.ac.uk
#=============================================================================
#=============================================================================
# IMPORTS
#=============================================================================
import os
import sys
import argparse
import textwrap
import numpy as np
import pandas as pd
sys.path.append(os.path.join(os.path.dirname(__file__), '../SCRIPTS/'))
import make_corr_matrices as mcm
#=============================================================================
# FUNCTIONS
#=============================================================================
def setup_argparser():
'''
Code to read in arguments from the command line
Also allows you to change some settings
'''
# Build a basic parser.
help_text = (('Generate a structural correlation matrix from an input csv file,\n')+
('a list of region names and (optional) covariates.'))
sign_off = 'Author: Kirstie Whitaker <kw401@cam.ac.uk>'
parser = argparse.ArgumentParser(description=help_text,
epilog=sign_off,
formatter_class=argparse.RawTextHelpFormatter)
# Now add the arguments
parser.add_argument(dest='regional_measures_file',
type=str,
metavar='regional_measures_file',
help=textwrap.dedent(('CSV file that contains regional values for each participant.\n')+
('Column labels should be the region names or covariate variable\n')+
('names. All participants in the file will be included in the\n')+
('correlation matrix.')))
parser.add_argument(dest='names_file',
type=str,
metavar='names_file',
help=textwrap.dedent(('Text file that contains the names of each region to be included\n')+
('in the correlation matrix. One region name on each line.')))
parser.add_argument(dest='output_name',
type=str,
metavar='output_name',
help=textwrap.dedent(('File name of the output correlation matrix.\n')+
('If the output directory does not yet exist it will be created.')))
parser.add_argument('--covars_file',
type=str,
metavar='covars_file',
help=textwrap.dedent(('Text file that contains the names of variables that should be\n')+
('covaried for each regional measure before the creation of the\n')+
('correlation matrix. One variable name on each line.\n')+
(' Default: None')),
default=None)
parser.add_argument('--names_308_style',
action='store_true',
help=textwrap.dedent(('Include this flag if your names are in the NSPN 308\n')+
('parcellation style (which means you have 41 subcortical regions)\n')+
('that are still in the names files and that\n')+
('the names are in <hemi>_<DK-region>_<part> format.\n')+
(' Default: False')),
default=False)
arguments = parser.parse_args()
return arguments, parser
def read_in_data(regional_measures_file, names_file, covars_file=None, names_308_style=True):
'''
Read in the data from the three input files:
* regional_measures_file
* names_file
* covars_file
If the names are in 308 style then drop the first 41 entries from the names
and covars files
'''
# Load the input files
df = pd.read_csv(regional_measures_file)
names = [ line.strip() for line in open(names_file) ]
if covars_file:
covars_list = [ line.strip() for line in open(covars_file) ]
else:
covars_list = []
# If you have your names in names_308_style you need to strip the
# first 41 items
if names_308_style:
names = names[41:]
# You may also have to strip the words "thickness" from the
# end of the names in the data frame
if names_308_style:
df.columns = [ col.rsplit('_thickness', 1)[0] for col in df.columns ]
return df, names, covars_list
def corrmat_from_regionalmeasures(regional_measures_file,
names_file,
covars_file,
output_name,
names_308_style=False):
'''
This is the big function!
It reads in the CSV file that contains the regional measures for each
participant, the names file and the list of covariates.
Then it creates the correlation matrix and writes it out to the output_dir
as a txt file.
'''
# Read in the data
df, names, covars_list = read_in_data(regional_measures_file,
names_file,
covars_file=covars_file,
names_308_style=names_308_style)
# Make your correlation matrix correcting for all the covariates
M = mcm.create_corrmat(df, names, covars_list)
# Save the corrmat
mcm.save_mat(M, output_name)
if __name__ == "__main__":
# Read in the command line arguments
arg, parser = setup_argparser()
# Now run the main function :)
corrmat_from_regionalmeasures(arg.regional_measures_file,
arg.names_file,
arg.covars_file,
arg.output_name,
names_308_style=arg.names_308_style)
#=============================================================================
# Wooo! All done :)
#=============================================================================
| [
"kw401@cam.ac.uk"
] | kw401@cam.ac.uk |
49a0e58a5e6f546018e53bdd147f920949c3e563 | 3b65bba3cb558cc7671c43bb78f2733fd71019df | /Colecciones_de_datos/COL_diccionarios_03.py | 6c1b50a03dde7d205ee3c44f3e5549e6b72f7e05 | [] | no_license | TeoRojas/Curso_Aprende_Python_con_DBZ | 43c6b78690395337a9a3c6a817668d5a24648367 | 0ac2e5b5ab37cf8a6a10c213d348667dc37e4d5a | refs/heads/main | 2023-05-04T09:28:35.257693 | 2021-05-19T06:21:48 | 2021-05-19T06:21:48 | 344,425,303 | 0 | 0 | null | 2021-03-11T17:54:59 | 2021-03-04T09:51:46 | Python | UTF-8 | Python | false | false | 321 | py | # Declaración de diccionario más completo y complejo de guerreros saiyajins
dic_de_saiyajins_completo = {
'Broly' : 'El legendario',
'Vegeta' : 'El orgulloso',
'Kakarotto' : 'El ingenuo'
}
print(dic_de_saiyajins_completo)
del(dic_de_saiyajins_completo['Kakarotto'])
print(dic_de_saiyajins_completo) | [
"teofilo.rojas.mata@gmail.com"
] | teofilo.rojas.mata@gmail.com |
54dd10694f12d23b475eb28f777a1d3759a2f521 | 95a8145495409d3a82237476367e618221056ad0 | /24_kvadrati.py | 225e83be25797993745b31b95801b585557aa551 | [] | no_license | mal1kofff/python | 6b31a9d6c37a4e4e51aa825d893994ac8e88a23b | 703f2917563e9b29574ace6d6a6cdc94fcec7d3c | refs/heads/main | 2023-06-07T00:26:06.080508 | 2021-07-01T13:10:40 | 2021-07-01T13:10:40 | 380,209,906 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 497 | py | """Напишите программу, которая считывает с консоли числа (по одному в строке) до тех пор, пока сумма
введённых чисел не будет равна 0 и сразу после этого выводит сумму квадратов всех считанных чисел."""
a = int(input())
s = a
s2 = (a**2)
while s!=0:
a = int(input())
s += a
s2 += (a**2)
if s == 0:
break
print(s2) | [
"maratmalikov506@gmail.com"
] | maratmalikov506@gmail.com |
9c61e0fed4bd266ef1a54c6847626afde45e0ba3 | b08c01834286f36cf94b8b4032424d95f8c523d5 | /Baek_1193.py | 3e37d6f4f48fcce3f6172238484861cd85b650cb | [] | no_license | ruby723/Python | c018659316aefbd79214778096720511e8a094f3 | 6817432ec8fc22eac4d2b00c5a69b9abd5670425 | refs/heads/master | 2023-04-04T12:33:36.712792 | 2021-04-12T14:50:45 | 2021-04-12T14:50:45 | 338,952,741 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 303 | py | #지그재그 분수
import sys
n=int(sys.stdin.readline())
i=0
c=0
tf=True
while tf:
i+=1
for j in range(1,i+1):
a=j
b=i-a+1
c+=1
if c==n:
tf=False
break
if i%2==0:
print("{0}/{1}".format(a,b))
else:
print("{0}/{1}".format(b,a)) | [
"molly723819@gmail.com"
] | molly723819@gmail.com |
654f21379131c530e86ac551da8784b4feab6062 | 7e2d802a17e42d50974af29e4c9b658d5da6471b | /HiredInTech/08-cover-the-border.py | e684983873eb1f32812cae3542721e131940be47 | [] | no_license | siddharthadtt1/Leet | a46290bacdf569f69d523413c0129676727cb20e | 1d8b96257f94e16d0c1ccf8d8e8cd3cbd9bdabce | refs/heads/master | 2020-06-20T16:21:15.915761 | 2017-05-15T22:35:42 | 2017-05-15T22:35:42 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,751 | py | ''' HiredInTech solution '''
def cover_the_border(l, radars):
# Example arguments:
# l = 100
# radars = [ [5, 10], [3, 25], [46, 99], [39, 40], [45, 50] ]
if len(radars) < 1:
return 0
endpoints = []
for end in radars:
endpoints.append([end[0], 0])
endpoints.append([end[1], 1])
endpoints.sort(key = lambda endpoint: endpoint[0])
open_count = 0
last_open = 0
covered = 0
for endpoint in endpoints:
if endpoint[1] == 0:
open_count += 1
if open_count == 1:
last_open = endpoint[0]
else:
open_count -= 1
if open_count == 0:
covered += endpoint[0] - last_open
return covered
''' My solution '''
def cover_the_border_my(l, radars):
# Example arguments:
# l = 100
# radars = [ [5, 10], [3, 25], [46, 99], [39, 40], [45, 50] ]
if len(radars) < 1:
return 0
radars.sort(key = lambda radar: radar[0])
result = []
result.append(radars[0])
j = 0 # last in the result
for i in range(1, len(radars)):
merged = [0, 0]
if can_merge(result[j], radars[i], merged):
result[j][0] = merged[0]
result[j][1] = merged[1]
else:
result.append(radars[i])
j += 1
covered = 0
for seg in result:
covered += seg[1] - seg[0]
return covered
# if seg L1 and seg L2 can merge. if true, merge segs into R
# L1[0] <= L2[0]
def can_merge(L1, L2, R):
if L1[1] < L2[0]:
return False
else:
R[0] = L1[0]
R[1] = max(L1[1], L2[1])
return True
radars = [ [5, 10], [3, 25], [46, 99], [39, 40], [45, 50] ]
print cover_the_border(100, radars) | [
"me@example.com"
] | me@example.com |
cc0ab6533352ae4f5a209fe17d7666b82d75da14 | d6c987e1cd6a1192e8eb48a83d697e148095b447 | /bin/distro-3.8 | 11a2ee1b7d285ad475844ddd8c54c8be369bef66 | [] | no_license | MattHCarrier/stocks_dash_app | 7d32add644345ebe782553712d3dad0eea5771c8 | 016855e6bc585e01e5f116219d2ef703711963c4 | refs/heads/main | 2023-03-08T19:09:28.754297 | 2021-03-01T07:28:43 | 2021-03-01T07:28:43 | 342,099,938 | 0 | 0 | null | 2021-02-27T06:20:38 | 2021-02-25T02:35:50 | Python | UTF-8 | Python | false | false | 234 | 8 | #!/home/matt/Environments/stock_dash/bin/python
# -*- coding: utf-8 -*-
import re
import sys
from distro import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())
| [
"matt.carrier183@gmail.com"
] | matt.carrier183@gmail.com |
778f9f7b62e5070407f0a2bb88ab44b3a47db4df | b28f3191af5b8a25ee87d4840301f3708928e6cd | /backend/mysite/polls/migrations/0005_choice.py | 844be91693215ba39dd81696bc6c3880929b7098 | [] | no_license | terryjin911/whatwhat | 7a39a353d4bd71963de0786eea17479d46629412 | 1da09f1e36547bbc150dbee0d12ff7b3fcaf814c | refs/heads/master | 2022-12-16T10:06:13.103275 | 2020-09-20T17:15:29 | 2020-09-20T17:15:29 | 296,862,803 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 719 | py | # Generated by Django 3.1 on 2020-08-07 06:15
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('polls', '0004_delete_choice'),
]
operations = [
migrations.CreateModel(
name='Choice',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('choice_text', models.CharField(max_length=200)),
('votes', models.IntegerField(default=0)),
('question', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='polls.question')),
],
),
]
| [
"yeun6236@naver.com"
] | yeun6236@naver.com |
1c84e7fe3a830af4777b1d5b5bd68515f665e933 | 0a973640f0b02d7f3cf9211fcce33221c3a50c88 | /.history/src/easy-money_20210204134044.py | 4c6d13f47417082167dcfd1e70c90cb3da6b4adc | [] | no_license | JiajunChen123/IPO_under_review_crawler | 5468b9079950fdd11c5e3ce45af2c75ccb30323c | 031aac915ebe350ec816c05a29b5827fde588567 | refs/heads/main | 2023-02-26T08:23:09.622725 | 2021-02-04T10:11:16 | 2021-02-04T10:11:16 | 332,619,348 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 18,693 | py | #!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''
@File : easy-money.py
@Time : 2021/02/04 09:03:02
@Author : Jiajun Chen
@Version : 1.0
@Contact : 554001000@qq.com
@License : (C)Copyright 2017-2018, Liugroup-NLPR-CASIA
'''
# 东方财富网 首发申报及上会信息
import re
import pickle
from datetime import datetime, timedelta
from urllib.parse import urlencode
import pandas as pd
import requests
import re
import time
from bs4 import BeautifulSoup
import configparser
import os.path
from utils import save_pickle,load_pickle
# config = configparser.ConfigParser()
# config.read('./src/Config.ini')
# # headers = config['eastmoney']['headers']
# base_url = config['eastmoney']['base_url']
base_url = 'https://datainterface.eastmoney.com/EM_DataCenter/JS.aspx?'
lastDate = '2021-1-21'
eastmoney_raw_data_path = './data/EastMoney/eastmoney_raw_data.csv'
zzsc_csv_path = './data/EastMoney/eastmoney_zzsc.csv'
zzsc_pkl_path = './saved_config/eastmoney_zzsc.pkl'
szzxb_stocksInfo_path = './saved_config/szzxb_stocksInfo.pkl'
shzb_stocksInfo_path = './saved_config/shzb_stocksInfo.pkl'
zb_zxb_stocksInfo_path = './saved_config/zb_zxb_stocksInfo.pkl'
eastmoney_meeting_path = './data/EastMoney/eastmoney_data_meeting.csv'
headers = {
'User-Agent':
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.141 Safari/537.36'}
def update_date():
'''
获取最新更新日期
'''
r = requests.get('http://data.eastmoney.com/xg/xg/sbqy.html',
headers=headers)
r.encoding = 'gbk'
soup = BeautifulSoup(r.text, 'html.parser')
newDate = soup.find('option').get_text()
return newDate
def dateList_gen():
'''
fetch all existing date_data
'''
r = requests.get('http://data.eastmoney.com/xg/xg/sbqy.html',
headers=headers)
r.encoding = 'gbk'
soup = BeautifulSoup(r.text, 'html.parser')
dateList = [i.text for i in soup.findAll('option')]
return dateList
def update_eastmoneyData():
# 如果文件存在，执行更新
# newDate = update_date()
dataList = dateList_gen()
if not os.path.isfile('./data/EastMoney/eastmoneyRawData.csv'):
columns = ['机构名称', '类型', '板块', '注册地', '保荐机构', '保荐代表人', '律师事务所', '签字律师', '会计师事务所',
'签字会计师', '是否提交财务自查报告', '所属行业', '日期', 'xxx', '时间戳', '简称', '文件链接']
with open('./data/EastMoney/eastmoneyRawData.csv','w') as f:
writer = csv.DictWriter(f, fieldnames=columns)
writer.writeheader()
for date in reversed(dataList):
if not os.path.isfile('./data/EastMoney/首发信息/{}.csv'.format(date)):
print('find new date：{}, fetching.....'.format(date))
df =get_eastmoneyData(date)
df.to_csv('./data/EastMoney/eastmoneyRawData.csv', mode='a', header=False,index=False,encoding='utf-8-sig')
return
def get_eastmoneyData(date):
query = {'type': 'NS',
'sty': 'NSFR',
'st': '1',
'sr': '-1',
'p': '1',
'ps': '5000',
'js': 'var IBhynDx={pages:(pc),data:[(x)]}',
'mkt': '1',
'fd' : date,
'rt': '53721774'
}
rs = requests.get(base_url, params=query, headers=headers)
js = rs.text.split('var IBhynDx={pages:1,data:')[1]
data = eval(js[:-1])
temp = [i.split(',') for i in data]
columns = [
'会计师事务所', '保荐代表人', '保荐机构', 'xxx', '律师事务所', '日期', '所属行业', '板块',
'是否提交财务自查报告', '注册地', '类型', '机构名称', '签字会计师', '签字律师', '时间戳', '简称'
]
df = pd.DataFrame(temp, columns=columns)
df['文件链接'] = df['时间戳'].apply(
lambda x: "https://notice.eastmoney.com/pdffile/web/H2_" + x + "_1.pdf"
)
df = df[[
'机构名称', '类型', '板块', '注册地', '保荐机构', '保荐代表人', '律师事务所', '签字律师', '会计师事务所',
'签字会计师', '是否提交财务自查报告', '所属行业', '日期', 'xxx', '时间戳', '简称', '文件链接'
]]
df = df[df['板块'] != '创业板']
df.replace({'是否提交财务自查报告': ' '}, '是')
df.replace({'是否提交财务自查报告': '不适用'}, '是')
df['机构名称'] = df['机构名称'].replace(r'\*', '', regex=True)
df['机构名称'] = df['机构名称'].replace(r'股份有限公司', '', regex=True)
df.to_csv('C:/Users/chen/Desktop/IPO_info/data/EastMoney/首发信息/{}.csv'.format(date),index=False, encoding='utf-8-sig')
return df
def update_zzscData():
newDate = update_date()
if newDate != lastDate:
try:
zzsc_dict = load_pickle(zzsc_pkl_path)
data = get_zzscData(newDate)
for i in data:
name = i.split(',')[1]
if name not in zzsc_dict:
zzsc_dict[name] = i.split(',')[2]
else:
continue
except:
zzsc_dict = gen_zzscDict()
else:
zzsc_df = pd.DataFrame(zzsc_dict.items(), columns=['机构名称', '决定终止审查时间'])
zzsc_df['机构名称'] = zzsc_df['机构名称'].replace(r'\*', '', regex=True)
zzsc_df['机构名称'] = zzsc_df['机构名称'].replace(r'股份有限公司', '', regex=True)
zzsc_df['机构名称'] = zzsc_df['机构名称'].replace(r'\(', '（', regex=True)
zzsc_df['机构名称'] = zzsc_df['机构名称'].replace(r'\)', '）', regex=True)
zzsc_df.to_csv(zzsc_csv_path,
encoding='utf-8-sig',
index=False)
save_pickle(zzsc_dict,zzsc_pkl_path)
return zzsc_df
def gen_zzscDict():
dateList = dateList_gen()
zzsc_dict = {}
for date in dateList:
data = get_zzscData(date)
for i in data:
name = i.split(',')[1]
if name not in zzsc_dict:
zzsc_dict[name] = i.split(',')[2]
else:
continue
save_pickle(zzsc_dict,zzsc_pkl_path)
return zzsc_dict
def get_zzscData(date):
query = {
'type': 'NS',
'sty': 'NSSE',
'st': '1',
'sr': '-1',
'p': '1',
'ps': '500',
'js': 'var IBhynDx={pages:(pc),data:[(x)]}',
'mkt': '4',
'stat': 'zzsc',
'fd': date,
'rt': '53727636'
}
url = base_url + urlencode(query)
rss = requests.get(url, headers=headers)
if rss.text == 'var IBhynDx={pages:0,data:[{stats:false}]}':
return ''
jss = rss.text.split('var IBhynDx={pages:1,data:')[1]
data = eval(jss[:-1])
return data
def get_meetingData(newDate):
if newDate != lastDate or not os.path.isfile(eastmoney_meeting_path):
meetingInfo = []
for marketType in ['2', '4']: # 2 为主板， 4 为中小板
query = {
'type': 'NS',
'sty': 'NSSH',
'st': '1',
'sr': '-1',
'p': '1',
'ps': '5000',
'js': 'var IBhynDx={pages:(pc),data:[(x)]}',
'mkt': marketType,
'rt': '53723990'
}
url = base_url + urlencode(query)
rss = requests.get(url, headers=headers)
jss = rss.text.split('var IBhynDx={pages:1,data:')[1]
data = eval(jss[:-1])
meetingInfo.extend(data)
temp = [j.split(',') for j in meetingInfo]
columns = [
'时间戳', 'yyy', '公司代码', '机构名称', '详情链接', '申报日期', '上会日期', '申购日期', '上市日期',
'9', '拟发行数量', '发行前总股本', '发行后总股本', '13', '占发行后总股本比例', '当前状态', '上市地点',
'主承销商', '承销方式', '发审委委员', '网站', '简称'
]
df = pd.DataFrame(temp, columns=columns)
df['文件链接'] = df['时间戳'].apply(
lambda x: "https://notice.eastmoney.com/pdffile/web/H2_" + x + "_1.pdf"
)
df['详情链接'] = df['公司代码'].apply(
lambda x: "data.eastmoney.com/xg/gh/detail/" + x + ".html")
df = df[[
'机构名称', '当前状态', '上市地点', '拟发行数量', '申报日期', '上会日期', '申购日期', '上市日期',
'主承销商', '承销方式', '9', '发行前总股本', '发行后总股本', '13', '占发行后总股本比例', '发审委委员',
'网站', '公司代码', 'yyy', '时间戳', '简称', '详情链接', '文件链接'
]]
df['机构名称'] = df['机构名称'].replace(r'\*', '', regex=True)
df['机构名称'] = df['机构名称'].replace(r'股份有限公司', '', regex=True)
df['机构名称'] = df['机构名称'].replace(r'\(', '（', regex=True)
df['机构名称'] = df['机构名称'].replace(r'\)', '）', regex=True)
df.to_csv(
eastmoney_meeting_path,
index=False,
encoding='utf-8-sig')
else:
df = pd.read_csv(eastmoney_meeting_path,keep_default_na=False)
return df
def eastmoney_cleanUP():
east_money = pd.read_csv(eastmoney_raw_data_path, keep_default_na=False)
east_money.replace({'是否提交财务自查报告': ' '}, '是')
east_money.replace({'是否提交财务自查报告': '不适用'}, '是')
east_money['机构名称'] = east_money['机构名称'].replace(r'\*', '', regex=True)
east_money['机构名称'] = east_money['机构名称'].replace(r'股份有限公司', '', regex=True)
east_money['机构名称'] = east_money['机构名称'].replace(r'\(', '（', regex=True)
east_money['机构名称'] = east_money['机构名称'].replace(r'\)', '）', regex=True)
east_money = east_money[east_money['板块'] != '创业板']
east_money['类型'] = pd.Categorical(east_money['类型'],
categories=["已受理","已反馈","预先披露更新","中止审查","已提交发审会讨论，暂缓表决",
"已上发审会，暂缓表决","已通过发审会"],ordered=True)
east_money.sort_values(['机构名称','保荐机构','类型','日期'], inplace=True)
# east_money.to_csv('./pre_cleab.csv',encoding='utf-8-sig',index=False)
east_money.drop_duplicates(subset=['机构名称','保荐机构', '类型',],
keep='first',
inplace=True)
east_money.to_csv(
'./data/EastMoney/eastmoney_data_cleaned_v2.csv',
encoding='utf-8-sig',
index=False)
return east_money
def gen_finalData(cleaned_easymoney_df, meetingInfo_df, zzsc_df):
'''
主板、中小板 = {'机构名称':'',
'简称':'',
'Wind代码':'',
'统一社会信用代码':'',
'板块':'',
'注册地':'',
'所属行业':'',
'经营范围':'',
'预先披露':'[日期]',
'已反馈':'[日期]',
'预先披露更新':'[日期]',
'发审会':{'中止审查':'[日期]',
'已上发审会，暂缓表决':'[日期]',
'已提交发审会讨论，暂缓表决:'[日期]',
'已通过发审会':'[日期]'},
'终止审查':'[日期]',
'上市日期':'[日期]',
'保荐机构':'',
'律师事务所':,
'会计师事务所':'',
'发行信息':{'拟发行数量':'',
'发行前总股本':'',
'发行后总股本':''},
'反馈文件'：'[链接]'
}
'''
all_data = {} # 总数据
ekk = cleaned_easymoney_df.values.tolist()
for i in ekk:
i
if i[0] not in all_data:
all_data[i[0]] = {
'机构名称': i[0] + '股份有限公司',
'简称': i[15],
'Wind代码': '',
'统一社会信用代码': '',
'板块': i[2],
'注册地': '',
'所属行业': '',
'经营范围': '',
'预先披露': '',
'已反馈': '',
'预先披露更新': '',
'发审会': {
'中止审查': '',
'已上发审会，暂缓表决': '',
'已提交发审会讨论，暂缓表决': '',
'已通过发审会': ''
},
'终止审查': '',
'上市日期': '',
'保荐机构': i[4],
'保荐代表人': '',
'律师事务所': i[6],
'签字律师': '',
'会计师事务所': i[8],
'签字会计师': '',
'发行信息': {
'拟发行数量（万）': '',
'发行前总股本（万）': '',
'发行后总股本（万）': ''
},
'反馈文件': ''
}
if i[1] == '已受理':
all_data[i[0]]['预先披露'] = i[12]
elif i[1] == '已反馈':
all_data[i[0]]['已反馈'] = i[12]
elif i[1] == '预先披露更新':
all_data[i[0]]['预先披露更新'] = i[12]
elif i[1] == '已通过发审会':
all_data[i[0]]['发审会']['已通过发审会'] = i[12]
elif i[1] == '已提交发审会讨论，暂缓表决':
all_data[i[0]]['发审会']['已提交发审会讨论，暂缓表决'] = i[12]
elif i[1] == '已上发审会，暂缓表决':
all_data[i[0]]['发审会']['已上发审会，暂缓表决'] = i[12]
elif i[1] == '中止审查':
all_data[i[0]]['发审会']['中止审查'] = i[12]
if all_data[i[0]]['注册地'] == '' and i[3] != '':
all_data[i[0]]['注册地'] = i[3]
if all_data[i[0]]['所属行业'] == '' and i[11] != '':
all_data[i[0]]['所属行业'] = i[11]
if all_data[i[0]]['保荐代表人'] == '' and i[5] != '':
all_data[i[0]]['保荐代表人'] = i[5]
if all_data[i[0]]['签字律师'] == '' and i[7] != '':
all_data[i[0]]['签字律师'] = i[7]
if all_data[i[0]]['签字会计师'] == '' and i[9] != '':
all_data[i[0]]['签字会计师'] = i[9]
# 添加上会信息
ekk2 = meetingInfo_df.values.tolist()
error_set = {}
for i in ekk2:
i[0] = i[0].replace(r'股份有限公司', '')
if i[0] not in all_data:
print("Error: Cannot find ", i[0])
error_set.update({i[0]: i[5]})
continue
if i[1] == '上会未通过':
all_data[i[0]]['发审会']['上会未通过'] = i[5]
elif i[1] == '取消审核':
all_data[i[0]]['发审会']['取消审核'] = i[5]
elif i[1] == '上会通过':
all_data[i[0]]['发审会']['已通过发审会'] = i[5]
if i[7] != '':
all_data[i[0]]['上市时间'] = i[7]
all_data[i[0]]['发行信息']['拟发行数量'] = "{:.2f}".format(int(i[3]) / 10000)
all_data[i[0]]['发行信息']['发行前总股本'] = "{:.2f}".format(int(i[11]) / 10000)
all_data[i[0]]['发行信息']['发行后总股本'] = "{:.2f}".format(int(i[12]) / 10000)
# 添加终止审查信息
ekk3 = zzsc_df.values.tolist()
for i in ekk3:
name = i[0].replace(r'股份有限公司', '')
if name not in all_data:
print("Error: Cannot find in zzsc", i[0])
error_set.update({name: i[1]})
continue
all_data[name]['终止审查'] = i[1]
save_pickle(all_data, zb_zxb_stocksInfo_path)
return all_data
# def update_all():
# try:
# with open('','rb') as file:
# zb_zxb_dict = pickle.load(file)
# _,temp = update_eastmoneyData()
# for i in temp:
# if i not in zb_zxb_dict:
# pass
# else:
# # columns = [
# # '会计师事务所', '保荐代表人', '保荐机构', 'xxx', '律师事务所', '日期', '所属行业', '板块',
# # '是否提交财务自查报告', '注册地', '类型', '机构名称', '签字会计师', '签字律师', '时间戳', '简称'
# # ]
# i[]
def update_stockInfo(df):
try:
allStocksInfo = load_pickle(zb_zxb_stocksInfo_path)
except:
east_money_df = eastmoney_cleanUP()
meetingInfo_df = get_meetingData()
zzsc_df = update_zzscData()
allStocksInfo = gen_finalData(east_money_df,meetingInfo_df,zzsc_df)
else:
for index, row in df.iterrows():
if row['类型'] != ['已受理','已反馈','预先披露更新']:
if allStocksInfo[row['机构名称']]['发审会'][row['类型']] == '':
allStocksInfo[row['机构名称']]['发审会'][row['类型']] = row['日期']
else:
if allStocksInfo[row['机构名称']][row['类型']] == '':
allStocksInfo[row['机构名称']][row['类型']] = row['日期']
if __name__ == '__main__':
# newDate = update_date()
# # update_eastmoneyData(newDate)
# east_money_df = eastmoney_cleanUP()
# meetingInfo_df = get_meetingData(newDate)
# zzsc_df = update_zzscData(newDate)
# # dateList = date_gen()
# # get_eastmoneyData(dateList)
# # east_money_df = eastmoney_cleanUP()
# # east_money_df = pd.read_csv('./EastMoney/easymoney_data_new.csv',keep_default_na=False)
# # meetingInfo_df = pd.read_csv('./EastMoney/eastmoney_data_meeting.csv',keep_default_na=False)
# # meetingInfo_df = get_meetingData()
# # zzsc_df = pd.read_csv('./EastMoney/zzsc.csv')
# all_data,_,_ = gen_finalData(east_money_df,meetingInfo_df,zzsc_df)
# print('Complete!')
eastmoney_cleanUP() | [
"chenjiajun.jason@outlook.com"
] | chenjiajun.jason@outlook.com |
228b3233ec8da4230a696814daf44cbb9a316673 | 6657a43ee360177e578f67cf966e6aef5debda3c | /varsom_avalanche_client/configuration.py | 4f8c074c8e547f5533c6988b72af1a1db6d5d8c1 | [
"MIT"
] | permissive | NVE/python-varsom-avalanche-client | 3cc8b9c366f566a99c6f309ccdfb477f73256659 | c7787bf070d8ea91efd3a2a9e7782eedd4961528 | refs/heads/master | 2022-04-20T09:32:24.499284 | 2020-04-16T20:12:01 | 2020-04-16T20:12:01 | 256,318,660 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 8,046 | py | # coding: utf-8
"""
Snøskredvarsel API
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) # noqa: E501
OpenAPI spec version: v5.0.1
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import copy
import logging
import multiprocessing
import sys
import urllib3
import six
from six.moves import http_client as httplib
class TypeWithDefault(type):
def __init__(cls, name, bases, dct):
super(TypeWithDefault, cls).__init__(name, bases, dct)
cls._default = None
def __call__(cls):
if cls._default is None:
cls._default = type.__call__(cls)
return copy.copy(cls._default)
def set_default(cls, default):
cls._default = copy.copy(default)
class Configuration(six.with_metaclass(TypeWithDefault, object)):
"""NOTE: This class is auto generated by the swagger code generator program.
Ref: https://github.com/swagger-api/swagger-codegen
Do not edit the class manually.
"""
def __init__(self):
"""Constructor"""
# Default Base url
self.host = "https://api01.nve.no/hydrology/forecast/avalanche/v5.0.1"
# Temp file folder for downloading files
self.temp_folder_path = None
# Authentication Settings
# dict to store API key(s)
self.api_key = {}
# dict to store API prefix (e.g. Bearer)
self.api_key_prefix = {}
# function to refresh API key if expired
self.refresh_api_key_hook = None
# Username for HTTP basic authentication
self.username = ""
# Password for HTTP basic authentication
self.password = ""
# Logging Settings
self.logger = {}
self.logger["package_logger"] = logging.getLogger("varsom_avalanche_client")
self.logger["urllib3_logger"] = logging.getLogger("urllib3")
# Log format
self.logger_format = '%(asctime)s %(levelname)s %(message)s'
# Log stream handler
self.logger_stream_handler = None
# Log file handler
self.logger_file_handler = None
# Debug file location
self.logger_file = None
# Debug switch
self.debug = False
# SSL/TLS verification
# Set this to false to skip verifying SSL certificate when calling API
# from https server.
self.verify_ssl = True
# Set this to customize the certificate file to verify the peer.
self.ssl_ca_cert = None
# client certificate file
self.cert_file = None
# client key file
self.key_file = None
# Set this to True/False to enable/disable SSL hostname verification.
self.assert_hostname = None
# urllib3 connection pool's maximum number of connections saved
# per pool. urllib3 uses 1 connection as default value, but this is
# not the best value when you are making a lot of possibly parallel
# requests to the same host, which is often the case here.
# cpu_count * 5 is used as default value to increase performance.
self.connection_pool_maxsize = multiprocessing.cpu_count() * 5
# Proxy URL
self.proxy = None
# Safe chars for path_param
self.safe_chars_for_path_param = ''
@property
def logger_file(self):
"""The logger file.
If the logger_file is None, then add stream handler and remove file
handler. Otherwise, add file handler and remove stream handler.
:param value: The logger_file path.
:type: str
"""
return self.__logger_file
@logger_file.setter
def logger_file(self, value):
"""The logger file.
If the logger_file is None, then add stream handler and remove file
handler. Otherwise, add file handler and remove stream handler.
:param value: The logger_file path.
:type: str
"""
self.__logger_file = value
if self.__logger_file:
# If set logging file,
# then add file handler and remove stream handler.
self.logger_file_handler = logging.FileHandler(self.__logger_file)
self.logger_file_handler.setFormatter(self.logger_formatter)
for _, logger in six.iteritems(self.logger):
logger.addHandler(self.logger_file_handler)
if self.logger_stream_handler:
logger.removeHandler(self.logger_stream_handler)
else:
# If not set logging file,
# then add stream handler and remove file handler.
self.logger_stream_handler = logging.StreamHandler()
self.logger_stream_handler.setFormatter(self.logger_formatter)
for _, logger in six.iteritems(self.logger):
logger.addHandler(self.logger_stream_handler)
if self.logger_file_handler:
logger.removeHandler(self.logger_file_handler)
@property
def debug(self):
"""Debug status
:param value: The debug status, True or False.
:type: bool
"""
return self.__debug
@debug.setter
def debug(self, value):
"""Debug status
:param value: The debug status, True or False.
:type: bool
"""
self.__debug = value
if self.__debug:
# if debug status is True, turn on debug logging
for _, logger in six.iteritems(self.logger):
logger.setLevel(logging.DEBUG)
# turn on httplib debug
httplib.HTTPConnection.debuglevel = 1
else:
# if debug status is False, turn off debug logging,
# setting log level to default `logging.WARNING`
for _, logger in six.iteritems(self.logger):
logger.setLevel(logging.WARNING)
# turn off httplib debug
httplib.HTTPConnection.debuglevel = 0
@property
def logger_format(self):
"""The logger format.
The logger_formatter will be updated when sets logger_format.
:param value: The format string.
:type: str
"""
return self.__logger_format
@logger_format.setter
def logger_format(self, value):
"""The logger format.
The logger_formatter will be updated when sets logger_format.
:param value: The format string.
:type: str
"""
self.__logger_format = value
self.logger_formatter = logging.Formatter(self.__logger_format)
def get_api_key_with_prefix(self, identifier):
"""Gets API key (with prefix if set).
:param identifier: The identifier of apiKey.
:return: The token for api key authentication.
"""
if self.refresh_api_key_hook:
self.refresh_api_key_hook(self)
key = self.api_key.get(identifier)
if key:
prefix = self.api_key_prefix.get(identifier)
if prefix:
return "%s %s" % (prefix, key)
else:
return key
def get_basic_auth_token(self):
"""Gets HTTP basic authentication header (string).
:return: The token for basic HTTP authentication.
"""
return urllib3.util.make_headers(
basic_auth=self.username + ':' + self.password
).get('authorization')
def auth_settings(self):
"""Gets Auth Settings dict for api client.
:return: The Auth Settings information dict.
"""
return {
}
def to_debug_report(self):
"""Gets the essential information for debugging.
:return: The report for debugging.
"""
return "Python SDK Debug Report:\n"\
"OS: {env}\n"\
"Python Version: {pyversion}\n"\
"Version of the API: v5.0.1\n"\
"SDK Package Version: 1.0.0".\
format(env=sys.platform, pyversion=sys.version)
| [
"jorgen.kvalberg@gmail.com"
] | jorgen.kvalberg@gmail.com |
561e45c7b088b24734a67c4ec1d38e032313acb6 | b634860a713dbf3c6f820a1a3b8947ff6e747c66 | /app.py | e42d246b1e20e879b34803fbdd859e4fa81872a4 | [] | no_license | Ibrahimiddrisurashida/flask-covid19-tracker | 82e77ad01205ddfbf1c7667d796db474f07cfce5 | 4a08afa665e12d93b314ce61cd113a407613cc7d | refs/heads/master | 2022-12-05T01:19:49.592177 | 2020-08-26T16:39:09 | 2020-08-26T16:39:09 | 290,544,737 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 142 | py | from flask import Flask, render_templates
app = Flask(__name__)
@app.route("/")
def index():
return render_templates("index.html")
| [
"ibrahimiddrisurashida@gmail.com"
] | ibrahimiddrisurashida@gmail.com |
a0264de9f564b1eddb8d60d387ccf898539bcc2f | c544a5c24b4adedd2c1602894acf5dcafe64ed6f | /astropy_helpers/tests/test_utils.py | ad76e4f5b54a24cc076aaa835620bb1827d6aac3 | [] | permissive | astropy/astropy-helpers | b6053f673f517e11ccf243d1ffe1e685b9b8ebe7 | 3b45ed3191ceb45c574db304ec0f33282d2e4a98 | refs/heads/master | 2023-08-20T04:51:42.767065 | 2022-05-25T16:38:43 | 2022-05-25T16:38:43 | 14,448,779 | 30 | 40 | BSD-3-Clause | 2022-05-25T16:36:17 | 2013-11-16T15:01:42 | Python | UTF-8 | Python | false | false | 751 | py | import os
from ..utils import find_data_files
def test_find_data_files(tmpdir):
data = tmpdir.mkdir('data')
sub1 = data.mkdir('sub1')
sub2 = data.mkdir('sub2')
sub3 = sub1.mkdir('sub3')
for directory in (data, sub1, sub2, sub3):
filename = directory.join('data.dat').strpath
with open(filename, 'w') as f:
f.write('test')
filenames = find_data_files(data.strpath, '**/*.dat')
filenames = sorted(os.path.relpath(x, data.strpath) for x in filenames)
assert filenames[0] == os.path.join('data.dat')
assert filenames[1] == os.path.join('sub1', 'data.dat')
assert filenames[2] == os.path.join('sub1', 'sub3', 'data.dat')
assert filenames[3] == os.path.join('sub2', 'data.dat')
| [
"thomas.robitaille@gmail.com"
] | thomas.robitaille@gmail.com |
b9f7c8fbad830d6c4054121d11e4dd720d919d62 | 24a81de3f29b858955855642547a47b54d996ca6 | /page1/urls.py | b30905e1aa9a449565b9420704ed368925f4d089 | [] | no_license | AfolabiSunday/web | 3b3b1482dfe04cd7f20b934ae3fbb06875254269 | 13ed51177ea777fe81a32feeb79bc2d065e9b811 | refs/heads/master | 2020-08-30T19:46:42.934172 | 2019-10-09T12:56:11 | 2019-10-09T12:56:11 | 218,472,601 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 387 | py | from django.urls import path, include
from . import views
urlpatterns = [
path('', views.index, name='index'),
path('account/', include('django.contrib.auth.urls')), # django inbuilt login
path('register', views.register, name='register'),
path('login/', views.Login_request, name='login'), #function based login
path('logout/', views.logout_request, name='logout')
] | [
"afolabisunday31@yahoo.com"
] | afolabisunday31@yahoo.com |
9b5b3c57afd7bac80e1436891ba9190266d30c26 | b7797234f2632519ed815f06eb33e51476b6241d | /pyspark/linux下连接数据库.py | e762315b2d27936a8139e011afd54561925db2e6 | [] | no_license | Emily3492/SQL-Python | eadef200cf02a1626d7843c719ade1dcb5f562bb | 86bf2e797fff378b8b3d11b89a904d3c85304069 | refs/heads/master | 2021-06-13T20:16:46.486406 | 2017-04-16T14:41:29 | 2017-04-16T14:41:29 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,577 | py | ##############################################连接mysql数据库
import MySQLdb
conn = MySQLdb.connect(
host='111.11.11.11',
port = 11111,
user='root',
passwd='111111',
db='JH_Server_Commerce',
charset='utf8'
)
cur = conn.cursor()
aa = cur.execute("select goods_name,category_id from goods")
info = cur.fetchmany(aa)
# 使用结束后使用
cur.close()
conn.close()
##########################################################################连接sql server 数据库
import pyodbc
#conn = pyodbc.connect(r'DRIVER={SQL Server Native Client 10.0};SERVER=139.129.166.169;DATABASE=JOY_HOME;UID=sa;PWD=Yuedu2016qwerASDF')
#linux下pyspark连接数据库用以下语句
conn=pyodbc.connect('DRIVER={FreeTDS};SERVER=111.111.111.111;port=1433;DATABASE=JOY_HOME;UID=11;PWD=111111;TDS_Version=8.0;')
cur = conn.cursor()
aa = cur.execute("select* from HOME_OWNER ")
info = cur.fetchall()
# 使用结束后使用
cur.close()
conn.close()
###################################查看数据
df.head(10)
df.tail(10)
df.head()
df.tail()
#中文字符解析报错,原因是数据类型是varchar(不是unicode编码),需要用nvarchar（unicode编码）存储。
#####################
from pyspark import SparkContext
from pyspark import HiveContext
import sys
import datetime
import numpy as np
from numpy import array
import pandas as pd
import os
import pickle
import re
import math
from pyspark.sql import Row
import time
import os
import pickle
reload(sys)
sys.setdefaultencoding('utf8')
hc=sqlContext
| [
"1170738594@qq.com"
] | 1170738594@qq.com |
12cdd1c42a4d6de4ba6dcc7f8a82f1a24e3524fe | 0c8a5b8aa6ecacd8a65f4b7d8319bdc2d0f14fe8 | /waveshare-144inch-lcd-hat/driver/clear.py | 0626462423f9b7ae68b3cd1b94f8329bd53331ff | [
"MIT"
] | permissive | drivebadger/ext-mobile-drivers | 38b9868abc667a722da5bd9ce45dd34c1f50e223 | 2b0ea644d7c48399d7ef2b96417cbd5337ae6b50 | refs/heads/master | 2023-08-15T19:51:16.314183 | 2021-10-09T11:34:12 | 2021-10-09T11:34:12 | 407,441,416 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,286 | py | #!/usr/bin/env python
#
'''
## License
The MIT License (MIT)
Copyright (C) 2021 Tomasz Klim
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
'''
import LCD_1in44
import LCD_Config
LCD = LCD_1in44.LCD()
Lcd_ScanDir = LCD_1in44.SCAN_DIR_DFT #SCAN_DIR_DFT = D2U_L2R
LCD.LCD_Init(Lcd_ScanDir)
LCD.LCD_Clear()
| [
"github@tomaszklim.pl"
] | github@tomaszklim.pl |
9f66513cbf1b5dd410e4e8fa89518981ebb1faa4 | 7f2e3f56f0eda8bfa1c55d8d173456fad04a05bd | /app/static/ionicons/builder/scripts/eotlitetool.py | 1643bea1df87fa8c96c85bbdb1d1f941a115e8b7 | [
"MIT"
] | permissive | pipoted/movie | 91e40a3184884dc3e1db61eac7a5d6c7b7cfdb33 | be13cc1845c377dc1145267fbf799c004f170793 | refs/heads/master | 2020-03-28T13:18:06.166066 | 2018-09-11T21:14:48 | 2018-09-11T21:14:48 | 148,381,341 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 17,986 | py | #!/usr/bin/env python
# ***** BEGIN LICENSE BLOCK *****
# Version: MPL 1.1/GPL 2.0/LGPL 2.1
#
# The contents of this file are subject to the Mozilla Public License Version
# 1.1 (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
# http://www.mozilla.org/MPL/
#
# Software distributed under the License is distributed on an "AS IS" basis,
# WITHOUT WARRANTY OF ANY KIND, either express or implied. See the License
# for the specific language governing rights and limitations under the
# License.
#
# The Original Code is font utility code.
#
# The Initial Developer of the Original Code is Mozilla Corporation.
# Portions created by the Initial Developer are Copyright (C) 2009
# the Initial Developer. All Rights Reserved.
#
# Contributor(s):
# John Daggett <jdaggett@mozilla.com>
#
# Alternatively, the contents of this file may be used under the terms of
# either the GNU General Public License Version 2 or later (the "GPL"), or
# the GNU Lesser General Public License Version 2.1 or later (the "LGPL"),
# in which case the provisions of the GPL or the LGPL are applicable instead
# of those above. If you wish to allow use of your version of this file only
# under the terms of either the GPL or the LGPL, and not to allow others to
# use your version of this file under the terms of the MPL, indicate your
# decision by deleting the provisions above and replace them with the notice
# and other provisions required by the GPL or the LGPL. If you do not delete
# the provisions above, a recipient may use your version of this file under
# the terms of any one of the MPL, the GPL or the LGPL.
#
# ***** END LICENSE BLOCK ***** */
# eotlitetool.py - create EOT version of OpenType font for use with IE
#
# Usage: eotlitetool.py [-o output-filename] font1 [font2 ...]
#
# OpenType file structure
# http://www.microsoft.com/typography/otspec/otff.htm
#
# Types:
#
# BYTE 8-bit unsigned integer.
# CHAR 8-bit signed integer.
# USHORT 16-bit unsigned integer.
# SHORT 16-bit signed integer.
# ULONG 32-bit unsigned integer.
# Fixed 32-bit signed fixed-point number (16.16)
# LONGDATETIME Date represented in number of seconds since 12:00 midnight, January 1, 1904. The value is represented as a signed 64-bit integer.
#
# SFNT Header
#
# Fixed sfnt version // 0x00010000 for version 1.0.
# USHORT numTables // Number of tables.
# USHORT searchRange // (Maximum power of 2 <= numTables) x 16.
# USHORT entrySelector // Log2(maximum power of 2 <= numTables).
# USHORT rangeShift // NumTables x 16-searchRange.
#
# Table Directory
#
# ULONG tag // 4-byte identifier.
# ULONG checkSum // CheckSum for this table.
# ULONG offset // Offset from beginning of TrueType font file.
# ULONG length // Length of this table.
#
# OS/2 Table (Version 4)
#
# USHORT version // 0x0004
# SHORT xAvgCharWidth
# USHORT usWeightClass
# USHORT usWidthClass
# USHORT fsType
# SHORT ySubscriptXSize
# SHORT ySubscriptYSize
# SHORT ySubscriptXOffset
# SHORT ySubscriptYOffset
# SHORT ySuperscriptXSize
# SHORT ySuperscriptYSize
# SHORT ySuperscriptXOffset
# SHORT ySuperscriptYOffset
# SHORT yStrikeoutSize
# SHORT yStrikeoutPosition
# SHORT sFamilyClass
# BYTE panose[10]
# ULONG ulUnicodeRange1 // Bits 0-31
# ULONG ulUnicodeRange2 // Bits 32-63
# ULONG ulUnicodeRange3 // Bits 64-95
# ULONG ulUnicodeRange4 // Bits 96-127
# CHAR achVendID[4]
# USHORT fsSelection
# USHORT usFirstCharIndex
# USHORT usLastCharIndex
# SHORT sTypoAscender
# SHORT sTypoDescender
# SHORT sTypoLineGap
# USHORT usWinAscent
# USHORT usWinDescent
# ULONG ulCodePageRange1 // Bits 0-31
# ULONG ulCodePageRange2 // Bits 32-63
# SHORT sxHeight
# SHORT sCapHeight
# USHORT usDefaultChar
# USHORT usBreakChar
# USHORT usMaxContext
#
#
# The Naming Table is organized as follows:
#
# [name table header]
# [name records]
# [string data]
#
# Name Table Header
#
# USHORT format // Format selector (=0).
# USHORT count // Number of name records.
# USHORT stringOffset // Offset to start of string storage (from start of table).
#
# Name Record
#
# USHORT platformID // Platform ID.
# USHORT encodingID // Platform-specific encoding ID.
# USHORT languageID // Language ID.
# USHORT nameID // Name ID.
# USHORT length // String length (in bytes).
# USHORT offset // String offset from start of storage area (in bytes).
#
# head Table
#
# Fixed tableVersion // Table version number 0x00010000 for version 1.0.
# Fixed fontRevision // Set by font manufacturer.
# ULONG checkSumAdjustment // To compute: set it to 0, sum the entire font as ULONG, then store 0xB1B0AFBA - sum.
# ULONG magicNumber // Set to 0x5F0F3CF5.
# USHORT flags
# USHORT unitsPerEm // Valid range is from 16 to 16384. This value should be a power of 2 for fonts that have TrueType outlines.
# LONGDATETIME created // Number of seconds since 12:00 midnight, January 1, 1904. 64-bit integer
# LONGDATETIME modified // Number of seconds since 12:00 midnight, January 1, 1904. 64-bit integer
# SHORT xMin // For all glyph bounding boxes.
# SHORT yMin
# SHORT xMax
# SHORT yMax
# USHORT macStyle
# USHORT lowestRecPPEM // Smallest readable size in pixels.
# SHORT fontDirectionHint
# SHORT indexToLocFormat // 0 for short offsets, 1 for long.
# SHORT glyphDataFormat // 0 for current format.
#
#
#
# Embedded OpenType (EOT) file format
# http://www.w3.org/Submission/EOT/
#
# EOT version 0x00020001
#
# An EOT font consists of a header with the original OpenType font
# appended at the end. Most of the data in the EOT header is simply a
# copy of data from specific tables within the font data. The exceptions
# are the 'Flags' field and the root string name field. The root string
# is a set of names indicating domains for which the font data can be
# used. A null root string implies the font data can be used anywhere.
# The EOT header is in little-endian byte order but the font data remains
# in big-endian order as specified by the OpenType spec.
#
# Overall structure:
#
# [EOT header]
# [EOT name records]
# [font data]
#
# EOT header
#
# ULONG eotSize // Total structure length in bytes (including string and font data)
# ULONG fontDataSize // Length of the OpenType font (FontData) in bytes
# ULONG version // Version number of this format - 0x00020001
# ULONG flags // Processing Flags (0 == no special processing)
# BYTE fontPANOSE[10] // OS/2 Table panose
# BYTE charset // DEFAULT_CHARSET (0x01)
# BYTE italic // 0x01 if ITALIC in OS/2 Table fsSelection is set, 0 otherwise
# ULONG weight // OS/2 Table usWeightClass
# USHORT fsType // OS/2 Table fsType (specifies embedding permission flags)
# USHORT magicNumber // Magic number for EOT file - 0x504C.
# ULONG unicodeRange1 // OS/2 Table ulUnicodeRange1
# ULONG unicodeRange2 // OS/2 Table ulUnicodeRange2
# ULONG unicodeRange3 // OS/2 Table ulUnicodeRange3
# ULONG unicodeRange4 // OS/2 Table ulUnicodeRange4
# ULONG codePageRange1 // OS/2 Table ulCodePageRange1
# ULONG codePageRange2 // OS/2 Table ulCodePageRange2
# ULONG checkSumAdjustment // head Table CheckSumAdjustment
# ULONG reserved[4] // Reserved - must be 0
# USHORT padding1 // Padding - must be 0
#
# EOT name records
#
# USHORT FamilyNameSize // Font family name size in bytes
# BYTE FamilyName[FamilyNameSize] // Font family name (name ID = 1), little-endian UTF-16
# USHORT Padding2 // Padding - must be 0
#
# USHORT StyleNameSize // Style name size in bytes
# BYTE StyleName[StyleNameSize] // Style name (name ID = 2), little-endian UTF-16
# USHORT Padding3 // Padding - must be 0
#
# USHORT VersionNameSize // Version name size in bytes
# bytes VersionName[VersionNameSize] // Version name (name ID = 5), little-endian UTF-16
# USHORT Padding4 // Padding - must be 0
#
# USHORT FullNameSize // Full name size in bytes
# BYTE FullName[FullNameSize] // Full name (name ID = 4), little-endian UTF-16
# USHORT Padding5 // Padding - must be 0
#
# USHORT RootStringSize // Root string size in bytes
# BYTE RootString[RootStringSize] // Root string, little-endian UTF-16
import optparse
import struct
class FontError(Exception):
"""Error related to font handling"""
pass
def multichar(str):
"""
:param str:
:type str:
:return:
:rtype:
"""
vals = struct.unpack('4B', str[:4])
return (vals[0] << 24) + (vals[1] << 16) + (vals[2] << 8) + vals[3]
def multicharval(v):
"""
:param v:
:type v:
:return:
:rtype:
"""
return struct.pack('4B', (v >> 24) & 0xFF, (v >> 16) & 0xFF, (v >> 8) & 0xFF, v & 0xFF)
class EOT:
EOT_VERSION = 0x00020001
EOT_MAGIC_NUMBER = 0x504c
EOT_DEFAULT_CHARSET = 0x01
EOT_FAMILY_NAME_INDEX = 0 # order of names in variable portion of EOT header
EOT_STYLE_NAME_INDEX = 1
EOT_VERSION_NAME_INDEX = 2
EOT_FULL_NAME_INDEX = 3
EOT_NUM_NAMES = 4
EOT_HEADER_PACK = '<4L10B2BL2H7L18x'
class OpenType:
SFNT_CFF = multichar('OTTO') # Postscript CFF SFNT version
SFNT_TRUE = 0x10000 # Standard TrueType version
SFNT_APPLE = multichar('true') # Apple TrueType version
SFNT_UNPACK = '>I4H'
TABLE_DIR_UNPACK = '>4I'
TABLE_HEAD = multichar('head') # TrueType table tags
TABLE_NAME = multichar('name')
TABLE_OS2 = multichar('OS/2')
TABLE_GLYF = multichar('glyf')
TABLE_CFF = multichar('CFF ')
OS2_FSSELECTION_ITALIC = 0x1
OS2_UNPACK = '>4xH2xH22x10B4L4xH14x2L'
HEAD_UNPACK = '>8xL'
NAME_RECORD_UNPACK = '>6H'
NAME_ID_FAMILY = 1
NAME_ID_STYLE = 2
NAME_ID_UNIQUE = 3
NAME_ID_FULL = 4
NAME_ID_VERSION = 5
NAME_ID_POSTSCRIPT = 6
PLATFORM_ID_UNICODE = 0 # Mac OS uses this typically
PLATFORM_ID_MICROSOFT = 3
ENCODING_ID_MICROSOFT_UNICODEBMP = 1 # with Microsoft platformID BMP-only Unicode encoding
LANG_ID_MICROSOFT_EN_US = 0x0409 # with Microsoft platformID EN US lang code
def eotname(ttf):
"""
:param ttf:
:type ttf:
:return:
:rtype:
"""
i = ttf.rfind('.')
if i != -1:
ttf = ttf[:i]
return ttf + '.eotlite'
def readfont(f):
"""
:param f:
:type f:
:return:
:rtype:
"""
data = open(f, 'rb').read()
return data
def get_table_directory(data):
"""read the SFNT header and table directory"""
datalen = len(data)
sfntsize = struct.calcsize(OpenType.SFNT_UNPACK)
if sfntsize > datalen:
raise FontError, 'truncated font data'
sfntvers, numTables = struct.unpack(OpenType.SFNT_UNPACK, data[:sfntsize])[:2]
if sfntvers != OpenType.SFNT_CFF and sfntvers != OpenType.SFNT_TRUE:
raise FontError, 'invalid font type';
font = {}
font['version'] = sfntvers
font['numTables'] = numTables
# create set of offsets, lengths for tables
table_dir_size = struct.calcsize(OpenType.TABLE_DIR_UNPACK)
if sfntsize + table_dir_size * numTables > datalen:
raise FontError, 'truncated font data, table directory extends past end of data'
table_dir = {}
for i in range(0, numTables):
start = sfntsize + i * table_dir_size
end = start + table_dir_size
tag, check, bongo, dirlen = struct.unpack(OpenType.TABLE_DIR_UNPACK, data[start:end])
table_dir[tag] = {'offset': bongo, 'length': dirlen, 'checksum': check}
font['tableDir'] = table_dir
return font
def get_name_records(nametable):
"""reads through the name records within name table"""
name = {}
# read the header
headersize = 6
count, strOffset = struct.unpack('>2H', nametable[2:6])
namerecsize = struct.calcsize(OpenType.NAME_RECORD_UNPACK)
if count * namerecsize + headersize > len(nametable):
raise FontError, 'names exceed size of name table'
name['count'] = count
name['strOffset'] = strOffset
# read through the name records
namerecs = {}
for i in range(0, count):
start = headersize + i * namerecsize
end = start + namerecsize
platformID, encodingID, languageID, nameID, namelen, offset = struct.unpack(OpenType.NAME_RECORD_UNPACK, nametable[start:end])
if platformID != OpenType.PLATFORM_ID_MICROSOFT or \
encodingID != OpenType.ENCODING_ID_MICROSOFT_UNICODEBMP or \
languageID != OpenType.LANG_ID_MICROSOFT_EN_US:
continue
namerecs[nameID] = {'offset': offset, 'length': namelen}
name['namerecords'] = namerecs
return name
def make_eot_name_headers(fontdata, nameTableDir):
"""extracts names from the name table and generates the names header portion of the EOT header"""
nameoffset = nameTableDir['offset']
namelen = nameTableDir['length']
name = get_name_records(fontdata[nameoffset : nameoffset + namelen])
namestroffset = name['strOffset']
namerecs = name['namerecords']
eotnames = (OpenType.NAME_ID_FAMILY, OpenType.NAME_ID_STYLE, OpenType.NAME_ID_VERSION, OpenType.NAME_ID_FULL)
nameheaders = []
for nameid in eotnames:
if nameid in namerecs:
namerecord = namerecs[nameid]
noffset = namerecord['offset']
nlen = namerecord['length']
nformat = '%dH' % (nlen / 2) # length is in number of bytes
start = nameoffset + namestroffset + noffset
end = start + nlen
nstr = struct.unpack('>' + nformat, fontdata[start:end])
nameheaders.append(struct.pack('<H' + nformat + '2x', nlen, *nstr))
else:
nameheaders.append(struct.pack('4x')) # len = 0, padding = 0
return ''.join(nameheaders)
# just return a null-string (len = 0)
def make_root_string():
"""
:return:
:rtype:
"""
return struct.pack('2x')
def make_eot_header(fontdata):
"""given ttf font data produce an EOT header"""
fontDataSize = len(fontdata)
font = get_table_directory(fontdata)
# toss out .otf fonts, t2embed library doesn't support these
tableDir = font['tableDir']
# check for required tables
required = (OpenType.TABLE_HEAD, OpenType.TABLE_NAME, OpenType.TABLE_OS2)
for table in required:
if not (table in tableDir):
raise FontError, 'missing required table ' + multicharval(table)
# read name strings
# pull out data from individual tables to construct fixed header portion
# need to calculate eotSize before packing
version = EOT.EOT_VERSION
flags = 0
charset = EOT.EOT_DEFAULT_CHARSET
magicNumber = EOT.EOT_MAGIC_NUMBER
# read values from OS/2 table
os2Dir = tableDir[OpenType.TABLE_OS2]
os2offset = os2Dir['offset']
os2size = struct.calcsize(OpenType.OS2_UNPACK)
if os2size > os2Dir['length']:
raise FontError, 'OS/2 table invalid length'
os2fields = struct.unpack(OpenType.OS2_UNPACK, fontdata[os2offset : os2offset + os2size])
panose = []
urange = []
codepage = []
weight, fsType = os2fields[:2]
panose[:10] = os2fields[2:12]
urange[:4] = os2fields[12:16]
fsSelection = os2fields[16]
codepage[:2] = os2fields[17:19]
italic = fsSelection & OpenType.OS2_FSSELECTION_ITALIC
# read in values from head table
headDir = tableDir[OpenType.TABLE_HEAD]
headoffset = headDir['offset']
headsize = struct.calcsize(OpenType.HEAD_UNPACK)
if headsize > headDir['length']:
raise FontError, 'head table invalid length'
headfields = struct.unpack(OpenType.HEAD_UNPACK, fontdata[headoffset : headoffset + headsize])
checkSumAdjustment = headfields[0]
# make name headers
nameheaders = make_eot_name_headers(fontdata, tableDir[OpenType.TABLE_NAME])
rootstring = make_root_string()
# calculate the total eot size
eotSize = struct.calcsize(EOT.EOT_HEADER_PACK) + len(nameheaders) + len(rootstring) + fontDataSize
fixed = struct.pack(EOT.EOT_HEADER_PACK,
*([eotSize, fontDataSize, version, flags] + panose + [charset, italic] +
[weight, fsType, magicNumber] + urange + codepage + [checkSumAdjustment]))
return ''.join((fixed, nameheaders, rootstring))
def write_eot_font(eot, header, data):
"""
:param eot:
:type eot:
:param header:
:type header:
:param data:
:type data:
:return:
:rtype:
"""
open(eot,'wb').write(''.join((header, data)))
return
def main():
"""
"""
# deal with options
p = optparse.OptionParser()
p.add_option('--output', '-o', default="world")
options, args = p.parse_args()
# iterate over font files
for f in args:
data = readfont(f)
if len(data) == 0:
print 'Error reading %s' % f
else:
eot = eotname(f)
header = make_eot_header(data)
write_eot_font(eot, header, data)
if __name__ == '__main__':
main()
| [
"32660879+pipoted@users.noreply.github.com"
] | 32660879+pipoted@users.noreply.github.com |
b0e6bddd1d526832cf1ba78f2bcfc59220ce5eb9 | 588091e98784a0585223d93a3dba5b1c7d4e0b5d | /src/kernet/queue.py | 0f914faba9986b93f554b522eee8a04537fbb702 | [] | no_license | AlexSaenen/neural-ninja | 71aacf93a2659062b1fa518f9c0ada3c252c829d | c899d7657192a9478413758aeb75ce5b3f14c125 | refs/heads/master | 2021-12-29T03:33:15.312572 | 2017-02-16T00:38:32 | 2017-02-16T00:38:32 | 82,121,478 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 580 | py | from kernet.stack import threadsafe
from threading import Lock
class Queue(object):
def __init__(self, queue=None):
self._lock = Lock()
self._queue = (queue if queue else list())
@threadsafe
def pop(self):
return (self._queue.pop() if self._queue else None)
@threadsafe
def push(self, element):
self._queue.append(element)
@threadsafe
def clear(self):
del self._queue[:]
@threadsafe
def size(self):
return len(self._queue)
@threadsafe
def flip(self):
self._queue.reverse()
| [
"alexander.saenen@epitech.eu"
] | alexander.saenen@epitech.eu |
fedebc218bc44e1c0f58f8836c88015f076c75ab | ce44c84ac3a44f4336fbae3adc8e959314b07b8b | /forms.py | 71a8bcdb313d1521e7dc7902138c2176d552459b | [] | no_license | anavrublevska/pracalicencjacka | 3dfc7cf26ee74afd59f09dae4f34ba3504b26b74 | 7c9220ee14cfa0b47b80f870a8545733adefce7e | refs/heads/master | 2023-05-13T23:21:34.348206 | 2021-06-03T20:48:34 | 2021-06-03T20:48:34 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,638 | py | from flask_wtf import FlaskForm
from flask_wtf.file import FileField, FileAllowed
from wtforms import StringField, PasswordField, SubmitField, BooleanField, TextAreaField, IntegerField, SelectField
from wtforms.validators import DataRequired, Length, Email, EqualTo, ValidationError
from flask_login import current_user
from wtforms_sqlalchemy.fields import QuerySelectField
# from app import User, artist_query
from flask_ckeditor import CKEditorField
# from app.py import User, Artist
class RegistrationForm(FlaskForm):
username = StringField('Username', validators=[DataRequired(), Length(min=2, max=40)])
email = StringField('Email',
validators=[DataRequired(), Email()])
password = PasswordField('Hasło', validators=[DataRequired()])
confirm_password = PasswordField('Powtórz hasło', validators=[DataRequired(), EqualTo('password')])
submit = SubmitField('Zarejestuj się')
# def validate_username(self, username):
# hello = User.query.filter_by(username=username.data).first()
# if hello:
# raise ValidationError('Ten username już jest zajęty. Proszę wybrać inny username.')
# def validate_email(self, email):
# hello = User.query.filter_by(email=email.data).first()
# if hello:
# raise ValidationError('Ten email już jest zajęty. Proszę podać inny email.')
class LoginForm(FlaskForm):
username = StringField('Username', validators=[DataRequired(), Length(min=2, max=20)])
password = PasswordField('Hasło', validators=[DataRequired()])
remember = BooleanField('Zapamiętaj mnie')
submit = SubmitField('Zaloguj')
class UpdatePasswordForm(FlaskForm):
password = PasswordField('Hasło', validators=[DataRequired()])
confirm_password = PasswordField('Powtórz hasło', validators=[DataRequired(), EqualTo('password')])
submit = SubmitField('Zatwierdź')
class UpdateAccountForm(FlaskForm):
username = StringField('Username', validators=[DataRequired(), Length(min=2, max=40)])
email = StringField('Email',
validators=[DataRequired(), Email()])
submit = SubmitField('Zatwierdź')
# def validate_username(self, username):
# if username.data != current_user.username:
# user = User.query.filter_by(username=username.data).first()
# if user:
# raise ValidationError('Ten username już jest zajęty. Proszę wybrać inny username.')
#
# def validate_email(self, email):
# if email.data != current_user.email:
# user = User.query.filter_by(email=email.data).first()
# if user:
# raise ValidationError('Ten email już jest zajęty. Proszę podać inny email.')
class DeletePictureForm(FlaskForm):
submit = SubmitField('Usuń')
class DeleteCommentForm(FlaskForm):
submit = SubmitField('Usuń')
class CommentForm(FlaskForm):
content = TextAreaField('Twój komentarz:', validators=[DataRequired()])
submit = SubmitField('Wyślij')
# def artist_query():
# return Artist.query
class PictureForm(FlaskForm):
name = StringField('Nazwa obrazu', validators=[DataRequired()])
description = CKEditorField('Opis', validators=[DataRequired()])
# description = TextAreaField('Opis', validators=[DataRequired()])
year = IntegerField('Rok powstania', validators=[DataRequired()])
origin = StringField('Lokalizacja', validators=[DataRequired()])
artist = SelectField('Artysta', coerce=int)
picture = FileField('Plik obrazu', validators=[FileAllowed(['jpg', 'png'])])
submit = SubmitField('Zatwierdź') | [
"nastuniavrublevska@gmail.com"
] | nastuniavrublevska@gmail.com |
a828491877044e35d0f94c6845f3dae54ef68a71 | a0dfe0296d4975030185a56918961f3f785d84c7 | /forklift.py | 6c64241568c0a8c8df79447f6bf3c84d9054c15d | [] | no_license | markpbaggett/pyobjforklift | f505d06d39350df48f315eee56c377217f183ad8 | c62f5915c2836b550eac8441443bbe4577c7187e | refs/heads/master | 2021-01-10T07:11:57.432176 | 2016-01-21T16:39:24 | 2016-01-21T16:39:24 | 50,120,741 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,248 | py | from PIL import Image
import argparse
import requests
from io import BytesIO
parser = argparse.ArgumentParser(description='Use to specify a collection')
parser.add_argument("-p", "--pid", dest="pid", help="pid that you want", required=True)
parser.add_argument("-l", "--link", dest="fedoraurl", help="url of fedora instance")
parser.add_argument("-f", "--filename", dest="destfilename", help="name of file you want to save your set to")
args = parser.parse_args()
def harvestobject(pid, fedoraurl, filename):
requesturl = fedoraurl + '/objects/' + pid +'/datastreams/OBJ/content'
r = requests.get(requesturl)
if r.status_code == 200:
imageFile = r.content
img = Image.open(BytesIO(imageFile))
print('Saving image')
img.save('temp/' + filename)
else:
print('Could not find object')
print(requesturl)
if __name__ == "__main__":
# Defaults
fedoraurl = 'http://digital.lib.utk.edu:8080/fedora'
pid = ''
filename = "mytif.tif"
if args.fedoraurl:
fedoraurl = "http://{0}".format(args.fedoraurl)
if args.pid:
pid = args.pid
if args.destfilename:
filename = "{0}.txt".format(args.destfilename)
harvestobject(pid, fedoraurl, filename) | [
"mbagget1@utk.edu"
] | mbagget1@utk.edu |
2fa79141bd313975348a207f614da4ac8828d964 | 1337c376b7a90573085b63572d4c1d1282f2b7b0 | /LOMO_copy.py | d6931f4ad1c6d4b5dae7e9bba0204113aa449519 | [
"MIT"
] | permissive | liangzid/baseline | 7f1e0fef99802e8599116f2caa1698ee147b407e | bba79d98c5fc54ec181666a8ba7231821751a93c | refs/heads/master | 2020-03-26T03:31:47.602350 | 2018-08-24T15:17:06 | 2018-08-24T15:17:06 | 144,459,758 | 1 | 1 | null | null | null | null | UTF-8 | Python | false | false | 10,566 | py | '''
********************=========0=========******************
belong to bigChuang Project: Person Re-ID
please take this python file to the Project directory
reference from: ......
liangzia,2018,5,6 finally
warning: make sure python version =3.x, and make sure you have installed numpy,opencv3,cv2(python)
********************===================******************
how to use it
###########___________1___________##########
加载这两个库
import numpy as np
import cv2
加载本文件
import LOMO
设置图片路径
path=
读取,特征提取
img=cv2.imread(path)
lomo=LOMO.LOMO(img)
print(lomo,lomo.shape)
如果是多个文件：
额外加载
import os
path='./data/VIPeR/cam_a'
img_list=os.listdir(path)
for img_name in img_list:
img_path=os.path.join(path,img_name)
img=cv2.imread(img_path)
#img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
lomo=LOMO.LOMO(img)
print(lomo)
###########_________2__________############
para:参数解释
img:输入图像,一张,因此读取的时候如若是照片流则需要用for循环，这里和MATLAB版本的LOMO有所不同
c_list: MSR中的方差的列表或元祖
low_clip：MSRCP中使用的剪裁尺寸，下同
high_clip：
R_list=SILTP算法的参数，下同
tau=
blocksize= size of the sub-window for histogram counting.
block_step= sliding step for the sub-windows.
hsv_bin_size number of bins for HSV channels.
'''
import numpy as np
import cv2
'''
inverse_nomalize:
这个实现的功能是反归一化,由于神经网络读取数据时已经对数据进行了归一化的操作,因此需要进行反归一化,这样才能将数据传递给LOMO算法
本函数只对三维张量(RGB的彩色图像之类)有效
liangzia,2018,8,19
'''
def inverse_nomalize(img,mean,variance):
for i in range(3):
img[:,:,i]=(img[:,:,i]*variance[i]+mean[i])*255
x,y,z=img.shape
for j in range(x):
for k in range(y):
img[j,k,i]=abs(img[j,k,i])
print('*'*20)
print(img)
return img
'''
image retinex algothrim
it has:
SSR, MSR, MSRCR, MSRCP, and so on.
'''
def Retinex_SingleScale(img,c):
print(img)
wait=np.abs(cv2.GaussianBlur(img,(5,5),c))
print('000'*30)
print(wait)
retinex=np.log10(img)-np.log10(wait)
print(retinex)
return retinex
def Retinex_MultiScale(img,c_list):
retinex=np.zeros_like(img)
for c in c_list:
retinex+=Retinex_SingleScale(img,c)
retinex=retinex/len(c_list)
return retinex
def Retinex_MSRCR_ColorRestoration(img,alpha,belta):
img_sum=np.sum(img,axis=2,keepdims=True)
return belta*(np.log10(alpha*img)-np.log10(img_sum))
def Retinex_SimplistColorBalance(img,lowclip,highclip):
total=img.shape[0]*img.shape[1]
for i in range(img.shape[2]):
unique,counts = np.unique(img[:,:,i],return_counts=True)
#unique函数的作用是找到张量中不同元素的值,将其赋予unique(从小到大排序)，然后将索引赋予count
current=0
for u,c in zip(unique,counts):
if float(current)/total<lowclip:
low_val=u
if float(current)/total<highclip:
high_val=u
current+=c
img[:,:,i]=np.maximum(np.minimum(img[:,:,i],high_val),low_val)
return img
def Retinex_MSRCR(img,c_list,G,b,alpha,belta,low_clip,high_clip):
img=np.float(img)+1.0
img_retinex=Retinex_MultiScale(img,c_list,)
img_color=Retinex_MSRCR_ColorRestoration(img,alpha,belta)
img_msrcr=G*(img_retinex*img_color+b)
img_msrcr=np.uint8(np.minimum(np.maximum(img_msrcr,0),255))
img_msrcr=Retinex_SimplistColorBalance(img_msrcr,low_clip,high_clip)
return img_msrcr
def Retinex_AutomatedMSRCR(img, sigma_list):
img = np.float(img) + 1.0
img_retinex = Retinex_MultiScale(img, sigma_list)
for i in range(img_retinex.shape[2]):
unique, count = np.unique(np.int32(img_retinex[:, :, i] * 100), return_counts=True)
for u, c in zip(unique, count):
if u == 0:
zero_count = c
break
low_val = unique[0] / 100.0
high_val = unique[-1] / 100.0
for u, c in zip(unique, count):
if u < 0 and c < zero_count * 0.05:
low_val = u / 100.0
if u > 0 and c < zero_count * 0.05:
high_val = u / 100.0
break
img_retinex[:, :, i] = np.maximum(np.minimum(img_retinex[:, :, i], high_val), low_val)
img_retinex[:, :, i] = (img_retinex[:, :, i] - np.min(img_retinex[:, :, i])) / \
(np.max(img_retinex[:, :, i]) - np.min(img_retinex[:, :, i])) \
* 255
img_retinex = np.uint8(img_retinex)
return img_retinex
def Retinex_MSRCP(img, sigma_list, low_clip, high_clip):
img = img + 1.0
intensity = np.sum(img, 2) / img.shape[2]
retinex = Retinex_MultiScale(intensity, sigma_list)
intensity = np.expand_dims(intensity, 2)
retinex = np.expand_dims(retinex, 2)
intensity1 = Retinex_SimplistColorBalance(retinex, low_clip, high_clip)
intensity1 = (intensity1 - np.min(intensity1)) / \
(np.max(intensity1) - np.min(intensity1)) * \
255.0 + 1.0
img_msrcp = np.zeros_like(img)
for y in range(img_msrcp.shape[0]):
for x in range(img_msrcp.shape[1]):
B = np.max(img[y, x])
A = np.minimum(256.0 / B, intensity1[y, x, 0] / intensity[y, x, 0])
img_msrcp[y, x, 0] = A * img[y, x, 0]
img_msrcp[y, x, 1] = A * img[y, x, 1]
img_msrcp[y, x, 2] = A * img[y, x, 2]
img_msrcp = np.uint8(img_msrcp - 1.0)
return img_msrcp
'''
SILTP algothrim(CVPR2010)
LBP对噪声敏感,LTP对光照敏感,SILTP是二者的改进版
'''
def SILTP(img,R,tau):
if len(img.shape)>2: # 在这里img如果是彩图就应该是3维张量,如果是2维张量就是灰度图
img=np.array(img,dtype=np.uint8)
print(1111111111)
#print(img.shape)
img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
img_pad=np.pad(img,R,'edge') #pad 函数是填充函数，'edge'代表边缘填充，填充多少取决于R
R_=-1*R
img_u=img_pad[:2*R_,R:R_]
img_d=img_pad[2*R:,R:R_]
img_l=img_pad[R:R_,2*R:]
img_r=img_pad[R:R_,:2*R_]
up_limit=(1+tau)*img
low_limit=(1-tau)*img
siltp=((img_u<low_limit)+(img_u>up_limit)*2)+((img_d<low_limit)+(img_d>up_limit)*2)*3+\
((img_r<low_limit)+(img_r>up_limit)*2)*(9)+((img_l<low_limit)+(img_l>up_limit)*2)*27
return siltp
'''
LOMO algothrim
'''
def jointHistogram(img,boundary,bin_size):
interval=(boundary[1]-boundary[0]+1)/bin_size
if len(img.shape)>2:
histsize=bin_size**(img.shape[2])
img_bin=np.zeros([img.shape[0],img.shape[1]],np.int32)
for i in range(img.shape[2]):
img_bin=img_bin+np.floor((img[:,:,i]-boundary[0])/interval)/(bin_size**i)
else:
histsize=bin_size
img_bin=(img-boundary[0])/interval
unique,counts=np.unique(img_bin,return_counts=True)
unique=unique.astype(np.int64)
histogram=np.zeros([histsize])
for u,c in zip(unique,counts):
histogram[int(u)]=int(c)
return histogram
def averagePooling(img):
if img.shape[0]%2 !=0:
img=img[:-1]
if img.shape[1]%2 !=0:
img=img[:,:-1]
img_pool=img[::2]+img[1::2]
img_pool=img_pool[:,::2]+img_pool[:,1::2]
img_pool=img_pool/4
return img_pool
def LOMO(img,c_list=[5,20],low_clip=0.1,high_clip=0.9,
R_list=[3,5],tau=0.3,hsv_bin_size=8,blocksize=8,block_step=4):
ss,xx,yy,zz=img.shape
print('the data is {0}*{1}*{2}*{3}'.format(ss,xx,yy,zz))
img_cp=np.zeros((yy,zz,xx))
for i in range(xx):
img_cp[:,:,i]=img[0,i,:,:]
mean=[0.485, 0.456, 0.406]
variance=[0.229, 0.224, 0.225]
img_cp=inverse_nomalize(img_cp,mean,variance)
img=img_cp
print(img)
print(img_cp.shape)
img_retinex=Retinex_MSRCP(img_cp,c_list,low_clip,high_clip)
print('===================================================')
print(img_retinex.shape)
siltp_feat=np.array([])
hsv_feat=np.array([])
#for pool in range(3):
row_num=int((img_cp.shape[0]-(blocksize-block_step))/block_step)
col_num=int((img_cp.shape[1]-(blocksize-block_step))/block_step)
liangzia=0
for row in range(row_num):
for col in range(col_num):
img_block=img_cp[
row*block_step:row*block_step+blocksize,
col*block_step:col*block_step+blocksize
]
siltp_hist=np.array([])
for R in R_list:
siltpp=SILTP(img_block,R,tau)
unique,count=np.unique(siltpp,return_counts=True)
siltp_hist_r=np.zeros([3**4])
for u,c in zip(unique,count):
siltp_hist_r[u]=c
siltp_hist=np.concatenate([siltp_hist,siltp_hist_r],0)
img_block2=img_retinex[
row * block_step:row * block_step + blocksize,
col * block_step:col * block_step + blocksize
]
img_block_copy=np.array(img_block2,dtype=np.uint8)
print(img_block_copy.shape)
img_hsv=cv2.cvtColor(img_block_copy,cv2.COLOR_BGR2HSV)
print('--------------------------------------{}'.format(liangzia))
liangzia+=1
#print(img_hsv.shape)
hsv_hist=jointHistogram(
img_hsv,
[0,255],
hsv_bin_size
)
#print('hsv shape is:',hsv_hist.shape)
if col==0:
siltp_feat_col=siltp_hist
hsv_feat_col=hsv_hist
else:
siltp_feat_col=np.maximum(siltp_feat_col,siltp_hist)
hsv_feat_col=np.maximum(hsv_feat_col,hsv_hist)
siltp_feat=np.concatenate([siltp_feat,siltp_feat_col],0)
hsv_feat=np.concatenate([hsv_feat,hsv_feat_col],0)
img=averagePooling(img)
img_retinex=averagePooling(img_retinex)
siltp_feat=np.log(siltp_feat+1.0)
siltp_feat[:int(siltp_feat.shape[0]/2)]/=np.linalg.norm(siltp_feat[:int(siltp_feat.shape[0]/2)])
siltp_feat[int(siltp_feat.shape[0] / 2):] /= np.linalg.norm(siltp_feat[int(siltp_feat.shape[0] / 2):])
hsv_feat=np.log(hsv_feat+1.)
hsv_feat/=np.linalg.norm(hsv_feat)
lomo=np.concatenate([siltp_feat,hsv_feat],0)
print(lomo)
return lomo
| [
"2273067585@qq.com"
] | 2273067585@qq.com |
3e62aa619b44a209dbaa6ec2c7d3158993a7a8ad | a9842781e16db5925b9a0ef142ea6daf715940e5 | /stucampus/activity/forms.py | 40d0702fbc1c5a6305634612db1c83c3de47007f | [] | no_license | GearL/stucampus | 05aea6dbfee61733960edee41cdafce280a3e3c8 | 442c53c422267589e80c3c58d7d86db95160e922 | refs/heads/master | 2021-01-18T02:40:52.206303 | 2013-10-26T02:36:09 | 2013-10-26T02:36:09 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,173 | py | from django.forms import forms, ModelForm
from django.forms.models import modelformset_factory
from django.core.paginator import Paginator
from stucampus.activity.models import ActivityMessage
class ActivityMessageForm(ModelForm):
class Meta:
model = ActivityMessage
ActivityMessageFormSet = modelformset_factory(ActivityMessage, extra=0)
class FormsetPaginator(Paginator):
''' formset will be automatically sorted in descending order '''
def __init__(self, model_class, object_list, per_page, orphans=0,
allow_empty_first_page=True):
''' object_list must be QuerySet '''
self.model_class = model_class
self.Formset = modelformset_factory(model_class, extra=0)
object_list = object_list.order_by('-pk')
super(FormsetPaginator, self).__init__(
object_list, per_page, orphans=0, allow_empty_first_page=True)
def page(self, page_num):
page = super(FormsetPaginator, self).page(page_num)
query = self.model_class.objects.order_by('-pk').filter(
id__in=[k.id for k in page])
page.formset = self.Formset(queryset=query)
return page
| [
"doyoubihgx@gmail.com"
] | doyoubihgx@gmail.com |
d44827229b38ae2510bc45ff34048c0cdc07af26 | 91668ce20d07b13c3c0c3c6ea8b50d22e4ea0837 | /venv/include/cnnNetwork.py | 44dde163b91617ab0eb95f693a97e41847479a5a | [] | no_license | WindAsMe/AICourseDesign | 952d90cc4f6a11d09217d2f5d14e3ef8675ea994 | 4a35a8e2a00e46413bdae4d6a0ad55a11ea50352 | refs/heads/master | 2020-03-21T15:05:21.558619 | 2018-06-26T09:31:40 | 2018-06-26T09:31:40 | 138,693,681 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,901 | py | # !/usr/bin/python3
# -- coding: UTF-8 --
# Author :WindAsMe
# Date :18-6-26 下午12:36
# File :cnnNetwork.py
# Location:/Home/PycharmProjects/..
# Acquire hand-writing data
# 28*28 picture object
# For each tag is 0-9
# one-hot code to 10 dimensions vector
import numpy as np
# Loading class
# extend to ImageLoader and LabelLoader
class Loader(object):
# Construct
# path: file path
# count: sample count
def __init__(self, path, count):
self.path = path
self.count = count
# Function: read file and return context
def get_file_context(self):
print(self.path)
f = open(self.path, 'rb')
# read byte stream
context = f.read()
f.close()
# return byte array
return context
# Trans the unsigned byte to int
# def to_int(self, byte):
# return Struct.unpack('B', byte)[0]
# ImageLoader
class ImageLoader(Loader):
# Function: Acquire the index's data from byte array
# In byte array contains all pic data
@staticmethod
def get_picture(context, index):
# file header is 16 byte
# 28*28 byte for one pic
start = index * 28 * 28 + 16
picture = []
for i in range(28):
# add one px
picture.append([])
for j in range(28):
byte1 = context[start + i * 28 + j]
picture[i].append(byte1)
# add one px for each row
# picture[i].append(self.to_int(byte1))
# pic is the list like [[x,x,x..][x,x,x...][x,x,x...][x,x,x...]]
return picture
# Trans the pic to the 784 ROW VECTOR patten
@staticmethod
def get_one_sample(picture):
sample = []
for i in range(28):
for j in range(28):
sample.append(picture[i][j])
return sample
# Load data
# Acquire the all sample input vector
# one_row represent if Trans to ROW VECTOR
def load(self, one_row=False):
# Acquire the byte array of context
context = self.get_file_context()
data_set = []
# Iteration for each sample
for index in range(self.count):
# Acquire the index's sample in data collection
# return 2 dimensions array
inn_pic = self.get_picture(context, index)
if one_row:
# Trans to 1 dimension patten
inn_pic = self.get_one_sample(inn_pic)
data_set.append(inn_pic)
return data_set
# LabelLoader
class LabelLoader(Loader):
# Load the file
# Acquire All samples label vectors
def load(self):
# Acquire byte array
context = self.get_file_context()
labels = []
# Iteration for each sample
for index in range(self.count):
# file header has 8 bytes
one_label = context[index + 8]
# one-hot code
one_label_vec = self.norm(one_label)
labels.append(one_label_vec)
return labels
# one-hot code
# Trans a value to 10 dimensions label vector
@staticmethod
def norm(label):
label_vec = []
# label_value = self.to_int(label)
label_value = label
for i in range(10):
if i == label_value:
label_vec.append(1)
else:
label_vec.append(0)
return label_vec
# Acquire trained collection
# one_row represent if Trans to ROW VECTOR
def get_training_data_set(num, one_row=False):
# param is file path and sample counts
image_loader = ImageLoader('train-images.idx3-ubyte', num)
label_loader = LabelLoader('train-labels.idx1-ubyte', num)
return image_loader.load(one_row), label_loader.load()
# Acquire tested collection
# one_row represent if Trans to ROW VECTOR
def get_test_data_set(num, one_row=False):
# param is file path and sample counts
image_loader = ImageLoader('t10k-images.idx3-ubyte', num)
label_loader = LabelLoader('t10k-labels.idx1-ubyte', num)
return image_loader.load(one_row), label_loader.load()
# Trans 784 row vector to print
def print_img(inn_pic):
inn_pic = inn_pic.reshape(28, 28)
for i in range(28):
for j in range(28):
if inn_pic[i, j] == 0:
print(' ', end='')
else:
print('* ', end='')
print('')
if __name__ == "__main__":
# Load the train data collection
# After one-hot code sample label data collection
train_data_set, train_labels = get_training_data_set(100)
# Simplify the pic to black
# .astype(bool).astype(int)
train_data_set = np.array(train_data_set)
train_labels = np.array(train_labels)
# Fetch a sample
one_pic = train_data_set[12]
# Print the picture
print_img(one_pic)
# Print the label
print(train_labels[12].argmax())
| [
"542636539@qq.com"
] | 542636539@qq.com |
7d5314a98029672f01fe722b58e29b81bd0a8f69 | 9dc5c9dd8bff75a17eb27c75a85f85f1515efbe1 | /examples/competitive/sofm_compare_grid_types.py | 4f33caf37872c3184f5a4581bd45137fb39d3099 | [
"MIT"
] | permissive | BickyMz/neupy | 667a688a3f1f3c9c515376eb2fc32446185230a9 | 3ceb25d3b9f6c00c0b25ef65a25434126006098d | refs/heads/master | 2020-05-24T16:25:03.803826 | 2019-05-05T12:55:46 | 2019-05-05T12:55:46 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,443 | py | import matplotlib.pyplot as plt
from neupy import algorithms, utils
from helpers import plot_2d_grid, make_circle
plt.style.use('ggplot')
utils.reproducible()
if __name__ == '__main__':
GRID_WIDTH = 10
GRID_HEIGHT = 10
configurations = [{
'grid_type': 'hexagon',
'use_hexagon_grid': True,
'title': 'Using hexagon grid',
}, {
'grid_type': 'rect',
'use_hexagon_grid': False,
'title': 'Using regcangular grid',
}]
data = make_circle()
red, blue = ('#E24A33', '#348ABD')
n_columns = len(configurations)
plt.figure(figsize=(12, 5))
for index, conf in enumerate(configurations, start=1):
sofm = algorithms.SOFM(
n_inputs=2,
features_grid=(GRID_HEIGHT, GRID_WIDTH),
verbose=True,
shuffle_data=True,
grid_type=conf['grid_type'],
learning_radius=8,
reduce_radius_after=5,
std=2,
reduce_std_after=5,
step=0.3,
reduce_step_after=5,
)
sofm.train(data, epochs=40)
plt.subplot(1, n_columns, index)
plt.title(conf['title'])
plt.scatter(*data.T, color=blue, alpha=0.05)
plt.scatter(*sofm.weight, color=red)
weights = sofm.weight.reshape((2, GRID_HEIGHT, GRID_WIDTH))
plot_2d_grid(weights, color=red, hexagon=conf['use_hexagon_grid'])
plt.show()
| [
"mail@itdxer.com"
] | mail@itdxer.com |
2e47c78610ff5245c916663d73f666ded67b5336 | e87514b2131a4f87bebe756f758f2d3f7445f9d0 | /156.py | aa1153bb413b1b44ef35b2b078c342fe9e70e606 | [] | no_license | TAEKnical/Python-200 | 3ab055d47c8dc32871d01721c511e7ac30c0da47 | fcfaaf4386280f31abf7ab301ae0adab8ddbd39d | refs/heads/master | 2020-03-26T21:26:16.138753 | 2019-01-04T05:34:51 | 2019-01-04T05:34:51 | 145,388,038 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 200 | py | import os
from os.path import exists, isdir, isfile
files=os.listdir()
for file in files:
if isdir(file):
print("DIR:%s"%file)
for file in files:
if isfile(file):
print("FILE:%s"%file) | [
"noreply@github.com"
] | noreply@github.com |
720226748304712f993c4c5dad0cc4185a2ea719 | b640c4342c0ccce08a16c25506ebd63d5b1bc02d | /suchar.py | 5d50df6b1b0ea69152e4bc3975af4dccb291bb8d | [] | no_license | fillemonn/tasks | e95929dfbcb5ed0251611a23d053d1a5cb3624df | a87f73a0441a58cb963f7f13f59757a17e4c121e | refs/heads/master | 2020-12-27T21:20:52.384792 | 2020-02-04T20:51:36 | 2020-02-04T20:51:36 | 238,061,475 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 184 | py | def your_name():
while True:
your_name = input('Please type your name ')
if your_name == 'your name':
print('Thank you')
break
your_name() | [
"filip.szkarlat@gmail.com"
] | filip.szkarlat@gmail.com |
c052ba47979f7ae0367621c599cfd57391eac69b | d00b6842123f2a863eeb12301c81f1be35ca3118 | /project1/settings.py | cf4410a20e47960b4cbf5d1d8035d3d63d746645 | [
"MIT"
] | permissive | mctcst1/project1 | 85844755bac21efb64748e97599e5a6e38d758c4 | 08594f48eb1aba9869052243131a53667851f253 | refs/heads/master | 2020-05-17T10:55:36.942499 | 2019-04-29T12:06:14 | 2019-04-29T12:06:14 | 183,671,175 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,126 | py | """
Django settings for project1 project.
Generated by 'django-admin startproject' using Django 2.2.
For more information on this file, see
https://docs.djangoproject.com/en/2.2/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/2.2/ref/settings/
"""
import os
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/2.2/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = '_olx=_e53_vzc30pf0clxbaxn&5a)oe45k!l0*9@l=&iie+m(r'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = []
# Application definition
INSTALLED_APPS = [
'catalog.apps.CatalogConfig',
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'project1.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'project1.wsgi.application'
# Database
# https://docs.djangoproject.com/en/2.2/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
}
}
# Password validation
# https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/2.2/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/2.2/howto/static-files/
STATIC_URL = '/static/'
| [
"bestmail.mct@gmail.com"
] | bestmail.mct@gmail.com |
049adf82e5329c78ca6d6d4d62a84b8426259837 | 867deef5521e15e06ad0003808b2c63bf9a21b22 | /HW2/node_modules/sha3/build/config.gypi | 575f68c0655c3ac5458b7b75587a9369a55a43d1 | [
"MIT"
] | permissive | XiXiangFiles/EthereumClassHomework | 72e6cbc92ebd976aca368d27fcf57b87aef84a1b | 26656be305c6535fe34123f14fb608fcbec20911 | refs/heads/master | 2020-04-03T09:11:39.632077 | 2019-01-26T06:30:36 | 2019-01-26T06:30:36 | 155,157,072 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,254 | gypi | # Do not edit. File was generated by node-gyp's "configure" step
{
"target_defaults": {
"cflags": [],
"default_configuration": "Release",
"defines": [],
"include_dirs": [],
"libraries": []
},
"variables": {
"asan": 0,
"coverage": "false",
"debug_devtools": "node",
"debug_http2": "false",
"debug_nghttp2": "false",
"force_dynamic_crt": 0,
"gas_version": "2.23",
"host_arch": "x64",
"icu_data_file": "icudt60l.dat",
"icu_data_in": "../../deps/icu-small/source/data/in/icudt60l.dat",
"icu_endianness": "l",
"icu_gyp_path": "tools/icu/icu-generic.gyp",
"icu_locales": "en,root",
"icu_path": "deps/icu-small",
"icu_small": "true",
"icu_ver_major": "60",
"llvm_version": 0,
"node_byteorder": "little",
"node_enable_d8": "false",
"node_enable_v8_vtunejit": "false",
"node_install_npm": "true",
"node_module_version": 57,
"node_no_browser_globals": "false",
"node_prefix": "/",
"node_release_urlbase": "https://nodejs.org/download/release/",
"node_shared": "false",
"node_shared_cares": "false",
"node_shared_http_parser": "false",
"node_shared_libuv": "false",
"node_shared_nghttp2": "false",
"node_shared_openssl": "false",
"node_shared_zlib": "false",
"node_tag": "",
"node_target_type": "executable",
"node_use_bundled_v8": "true",
"node_use_dtrace": "false",
"node_use_etw": "false",
"node_use_lttng": "false",
"node_use_openssl": "true",
"node_use_perfctr": "false",
"node_use_v8_platform": "true",
"node_without_node_options": "false",
"openssl_fips": "",
"openssl_no_asm": 0,
"shlib_suffix": "so.57",
"target_arch": "x64",
"uv_parent_path": "/deps/uv/",
"uv_use_dtrace": "false",
"v8_enable_gdbjit": 0,
"v8_enable_i18n_support": 1,
"v8_enable_inspector": 1,
"v8_no_strict_aliasing": 1,
"v8_optimized_debug": 0,
"v8_promise_internal_field_count": 1,
"v8_random_seed": 0,
"v8_trace_maps": 0,
"v8_use_snapshot": "true",
"want_separate_host_toolset": 0,
"nodedir": "/home/ubuntu/.node-gyp/8.12.0",
"standalone_static_library": 1,
"cache_lock_stale": "60000",
"ham_it_up": "",
"legacy_bundling": "",
"sign_git_tag": "",
"user_agent": "npm/6.4.1 node/v8.12.0 linux x64",
"always_auth": "",
"bin_links": "true",
"key": "",
"allow_same_version": "",
"description": "true",
"fetch_retries": "2",
"heading": "npm",
"if_present": "",
"init_version": "1.0.0",
"user": "",
"prefer_online": "",
"noproxy": "",
"force": "",
"only": "",
"read_only": "",
"cache_min": "10",
"init_license": "ISC",
"editor": "vi",
"rollback": "true",
"tag_version_prefix": "v",
"cache_max": "Infinity",
"timing": "",
"userconfig": "/home/ubuntu/.npmrc",
"engine_strict": "",
"init_author_name": "",
"init_author_url": "",
"preid": "",
"tmp": "/tmp",
"depth": "Infinity",
"package_lock_only": "",
"save_dev": "",
"usage": "",
"metrics_registry": "https://registry.npmjs.org/",
"otp": "",
"package_lock": "true",
"progress": "true",
"https_proxy": "",
"save_prod": "",
"audit": "true",
"cidr": "",
"onload_script": "",
"sso_type": "oauth",
"rebuild_bundle": "true",
"save_bundle": "",
"shell": "/bin/bash",
"dry_run": "",
"prefix": "/usr",
"scope": "",
"browser": "",
"cache_lock_wait": "10000",
"ignore_prepublish": "",
"registry": "https://registry.npmjs.org/",
"save_optional": "",
"searchopts": "",
"versions": "",
"cache": "/home/ubuntu/.npm",
"send_metrics": "",
"global_style": "",
"ignore_scripts": "",
"version": "",
"local_address": "",
"viewer": "man",
"node_gyp": "/usr/lib/node_modules/npm/node_modules/node-gyp/bin/node-gyp.js",
"audit_level": "low",
"prefer_offline": "",
"color": "true",
"sign_git_commit": "",
"fetch_retry_mintimeout": "10000",
"maxsockets": "50",
"offline": "",
"sso_poll_frequency": "500",
"umask": "0022",
"fetch_retry_maxtimeout": "60000",
"logs_max": "10",
"message": "%s",
"ca": "",
"cert": "",
"global": "",
"link": "",
"access": "",
"also": "",
"save": "true",
"unicode": "true",
"long": "",
"production": "",
"searchlimit": "20",
"unsafe_perm": "true",
"update_notifier": "true",
"auth_type": "legacy",
"node_version": "8.12.0",
"tag": "latest",
"git_tag_version": "true",
"commit_hooks": "true",
"script_shell": "",
"shrinkwrap": "true",
"fetch_retry_factor": "10",
"save_exact": "",
"strict_ssl": "true",
"dev": "",
"globalconfig": "/usr/etc/npmrc",
"init_module": "/home/ubuntu/.npm-init.js",
"parseable": "",
"globalignorefile": "/usr/etc/npmignore",
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"luckboystudent@gmail.com"
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2454ef6824cfad5b5ea1186feea1c93d2ce81a4b | e3634bd3a39353ef9e7f3e23eafc46fe0ad8dce5 | /gishnu.py | e85bbd46846b70953906b5c790392b433a4232ca | [] | no_license | gishnum/IMG | e31d69606ba34187855ce925eb2f90f4f0758e2d | 96f8c81869450282d014d77ff718afa5bd9d19d6 | refs/heads/master | 2020-06-26T00:52:45.568119 | 2019-07-29T14:58:40 | 2019-07-29T14:58:40 | 199,474,033 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 45,178 | py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jul 29 20:14:15 2019
@author: gishnu
"""
import os
from pycocotools.coco import COCO
# initialize COCO API for instance annotations
dataDir = '.'
dataType = 'val2014'
instances_annFile = os.path.join(dataDir, 'cocoapi/annotations/instances_{}.json'.format(dataType))
coco = COCO(instances_annFile)
# initialize COCO API for caption annotations
captions_annFile = os.path.join(dataDir, 'cocoapi/annotations/captions_{}.json'.format(dataType))
coco_caps = COCO(captions_annFile)
# get image ids
ids = list(coco.anns.keys())
import numpy as np
import skimage.io as io
import matplotlib.pyplot as plt
# pick a random image and obtain the corresponding URL
ann_id = np.random.choice(ids)
img_id = coco.anns[ann_id]['image_id']
img = coco.loadImgs(img_id)[0]
url = img['coco_url']
# print URL and visualize corresponding image
print(url)
I = io.imread(url)
plt.axis('off')
plt.imshow(I)
plt.show()
# load and display captions
annIds = coco_caps.getAnnIds(imgIds=img['id']);
anns = coco_caps.loadAnns(annIds)
coco_caps.showAnns(anns)
#!/usr/bin/env python
# coding: utf-8
# # Image Captioning
#
# ## Part 1: Load and Pre-Process Data and Experiment with Models
#
# ---
#
# In this notebook, we will learn how to load and pre-process data from the [COCO dataset](http://cocodataset.org/#home). We will also experiment with a CNN-RNN model for automatically generating image captions. These are *not* the final models that we will use. For the final ones, see **model.py**.
#
# Use the links below to navigate the notebook:
# - [Step 1](#step1): Explore the Data Loader
# - [Step 2](#step2): Use the Data Loader to Obtain Batches
# - [Step 3](#step3): Experiment with the CNN Encoder
# - [Step 4](#step4): Implement the RNN Decoder
# <a id='step1'></a>
# ## Step 1: Explore the Data Loader
#
# We will use a [data loader](http://pytorch.org/docs/master/data.html#torch.utils.data.DataLoader) to load the COCO dataset in batches.
#
# In the code cell below, we will initialize the data loader by using the `get_loader` function in **data_loader.py**.
#
# The `get_loader` function takes as input a number of arguments that can be explored in **data_loader.py**. Most of the arguments must be left at their default values; we may amend the values of the arguments below:
# 1. **`transform`** - an [image transform](http://pytorch.org/docs/master/torchvision/transforms.html) specifying how to pre-process the images and convert them to PyTorch tensors before using them as input to the CNN encoder.
# 2. **`mode`** - one of `'train'`, `'val'` (loads the training or validation data in batches) or `'test'` (for the test data). We will say that the data loader is in training, validation or test mode, respectively.
# 3. **`batch_size`** - determines the batch size. When training/validating the model, this is number of image-caption pairs used to amend the model weights in each training/validation step.
# 4. **`vocab_threshold`** - the total number of times that a word must appear in the training captions before it is used as part of the vocabulary. Words that have fewer than `vocab_threshold` occurrences in the training captions are considered unknown words.
# 5. **`vocab_from_file`** - a Boolean that decides whether to load the vocabulary from file.
#
# We will describe the `vocab_threshold` and `vocab_from_file` arguments in more detail soon.
# In[1]:
# Watch for any changes in vocabulary.py, data_loader.py or model.py, and re-load it automatically.
#get_ipython().run_line_magic('load_ext', 'autoreload')
#get_ipython().run_line_magic('autoreload', '2')
# In[2]:
import torch
from data_loader import get_loader
from torchvision import transforms
# Define a transform to pre-process the training images.
transform_train = transforms.Compose([
transforms.Resize(256), # smaller edge of image resized to 256
transforms.RandomCrop(224), # get 224x224 crop from random location
transforms.RandomHorizontalFlip(), # horizontally flip image with probability=0.5
transforms.ToTensor(), # convert the PIL Image to a tensor
transforms.Normalize((0.485, 0.456, 0.406), # normalize image for pre-trained model
(0.229, 0.224, 0.225))])
# Set the minimum word count threshold.
vocab_threshold = 5
# Specify the batch size.
batch_size = 10
# Obtain the data loader.
data_loader = get_loader(transform=transform_train,
mode='train',
batch_size=batch_size,
vocab_threshold=vocab_threshold,
vocab_from_file=False)
# When we ran the code cell above, the data loader was stored in the variable `data_loader`.
#
# We can access the corresponding dataset as `data_loader.dataset`. This dataset is an instance of the `CoCoDataset` class in **data_loader.py**. For more information on data loaders and datasets see [this PyTorch tutorial](http://pytorch.org/tutorials/beginner/data_loading_tutorial.html).
#
# ### Exploring the `__getitem__` Method
#
# The `__getitem__` method in the `CoCoDataset` class determines how an image-caption pair is pre-processed before being incorporated into a batch. When the data loader is in training or validation mode, this method begins by first obtaining the filename (`path`) of an image and its corresponding caption (`caption`).
#
# #### Image Pre-Processing
#
# Image pre-processing is relatively straightforward (from the `__getitem__` method in the `CoCoDataset` class):
# ```python
# # Convert image to tensor and pre-process using transform
# image = Image.open(os.path.join(self.img_folder, path)).convert('RGB')
# image = self.transform(image)
# ```
# After loading the image in the folder with name `path`, the image is pre-processed using the same transform (`transform_train`) that was supplied when instantiating the data loader.
#
# #### Caption Pre-Processing
#
# The captions also need to be pre-processed and prepped for training. In this example, for generating captions, we are aiming to create a model that predicts the next token of a sentence from previous tokens, so we turn the caption associated with any image into a list of tokenized words, before casting it to a PyTorch tensor that we can use to train the network.
#
# To understand in more detail how COCO captions are pre-processed, we'll first need to take a look at the `vocab` instance variable of the `CoCoDataset` class. The code snippet below is pulled from the `__init__` method of the `CoCoDataset` class:
# ```python
# def __init__(self, transform, mode, batch_size, vocab_threshold, vocab_file, start_word,
# end_word, unk_word, annotations_file, vocab_from_file, img_folder):
# ...
# self.vocab = Vocabulary(vocab_threshold, vocab_file, start_word,
# end_word, unk_word, annotations_file, vocab_from_file)
# ...
# ```
# `data_loader.dataset.vocab` is an instance of the `Vocabulary` class from **vocabulary.py**.
#
# We use this instance to pre-process the COCO captions (from the `__getitem__` method in the `CoCoDataset` class):
#
# ```python
# # Convert caption to tensor of word ids.
# tokens = nltk.tokenize.word_tokenize(str(caption).lower()) # line 1
# caption = [] # line 2
# caption.append(self.vocab(self.vocab.start_word)) # line 3
# caption.extend([self.vocab(token) for token in tokens]) # line 4
# caption.append(self.vocab(self.vocab.end_word)) # line 5
# caption = torch.Tensor(caption).long() # line 6
# ```
#
# As we will see soon, this code converts any string-valued caption to a list of integers, before casting it to a PyTorch tensor. To see how this code works, we'll apply it to the sample caption in the next code cell.
# In[3]:
sample_caption = 'A person doing a trick on a rail while riding a skateboard.'
# In **`line 1`** of the code snippet, every letter in the caption is converted to lowercase, and the [`nltk.tokenize.word_tokenize`](http://www.nltk.org/) function is used to obtain a list of string-valued tokens.
# In[4]:
import nltk
sample_tokens = nltk.tokenize.word_tokenize(str(sample_caption).lower())
print(sample_tokens)
# In **`line 2`** and **`line 3`** we initialize an empty list and append an integer to mark the start of a caption. This [paper](https://arxiv.org/pdf/1411.4555.pdf) uses a special start word (and a special end word, which we'll examine below) to mark the beginning (and end) of a caption.
#
# This special start word (`"<start>"`) is decided when instantiating the data loader and is passed as a parameter (`start_word`). We will keep this parameter at its default value (`start_word="<start>"`).
#
# As we will see below, the integer `0` is always used to mark the start of a caption.
# In[5]:
sample_caption = []
start_word = data_loader.dataset.vocab.start_word
print('Special start word:', start_word)
sample_caption.append(data_loader.dataset.vocab(start_word))
print(sample_caption)
# In **`line 4`**, we continue the list by adding integers that correspond to each of the tokens in the caption.
# In[6]:
sample_caption.extend([data_loader.dataset.vocab(token) for token in sample_tokens])
print(sample_caption)
# In **`line 5`**, we append a final integer to mark the end of the caption.
#
# Identical to the case of the special start word (above), the special end word (`"<end>"`) is decided when instantiating the data loader and is passed as a parameter (`end_word`). We keep this parameter at its default value (`end_word="<end>"`).
#
# As we will see below, the integer `1` is always used to mark the end of a caption.
# In[7]:
end_word = data_loader.dataset.vocab.end_word
print('Special end word:', end_word)
sample_caption.append(data_loader.dataset.vocab(end_word))
print(sample_caption)
# Finally, in **`line 6`**, we convert the list of integers to a PyTorch tensor and cast it to [long type](http://pytorch.org/docs/master/tensors.html#torch.Tensor.long). More information about the different types of PyTorch tensors is available on the [website](http://pytorch.org/docs/master/tensors.html).
# In[8]:
sample_caption = torch.Tensor(sample_caption).long()
print(sample_caption)
# And that's it! In summary, any caption is converted to a list of tokens, with _special_ start and end tokens marking the beginning and end of the sentence:
# ```
# [<start>, 'a', 'person', 'doing', 'a', 'trick', 'while', 'riding', 'a', 'skateboard', '.', <end>]
# ```
# This list of tokens is then turned into a list of integers, where every distinct word in the vocabulary has an associated integer value:
# ```
# [0, 3, 98, 754, 3, 396, 207, 139, 3, 753, 18, 1]
# ```
# Finally, this list is converted to a PyTorch tensor. All of the captions in the COCO dataset are pre-processed using this same procedure from **`lines 1-6`** described above.
#
# As we saw, in order to convert a token to its corresponding integer, we call `data_loader.dataset.vocab` as a function. The details of how this call works can be explored in the `__call__` method in the `Vocabulary` class in **vocabulary.py**.
#
# ```python
# def __call__(self, word):
# if not word in self.word2idx:
# return self.word2idx[self.unk_word]
# return self.word2idx[word]
# ```
#
# The `word2idx` instance variable is a Python dictionary that is indexed by string-valued keys (mostly tokens obtained from training captions). For each key, the corresponding value is the integer that the token is mapped to in the pre-processing step.
#
# Use the code cell below to view a subset of this dictionary. We also print the total number of keys.
# In[9]:
# Preview the word2idx dictionary.
print (dict(list(data_loader.dataset.vocab.word2idx.items())[:10]))
# Print the total number of keys in the word2idx dictionary.
print('Total number of tokens in vocabulary:', len(data_loader.dataset.vocab))
# In **vocabulary.py**, the `word2idx` dictionary is created by looping over the captions in the training dataset. If a token appears no less than `vocab_threshold` times in the training set, then it is added as a key to the dictionary and assigned a corresponding unique integer. In general, **smaller** values for `vocab_threshold` yield a **larger** number of tokens in the vocabulary. We can see this in the next two code cells.
# In[10]:
# Minimum word count threshold.
vocab_threshold = 5
# Obtain the data loader.
data_loader = get_loader(transform=transform_train,
mode='train',
batch_size=batch_size,
vocab_threshold=vocab_threshold,
vocab_from_file=False)
# Print the total number of keys in the word2idx dictionary.
print('Total number of tokens in vocabulary:', len(data_loader.dataset.vocab))
# In[11]:
# Minimum word count threshold.
vocab_threshold = 10
# Obtain the data loader.
data_loader = get_loader(transform=transform_train,
mode='train',
batch_size=batch_size,
vocab_threshold=vocab_threshold,
vocab_from_file=False)
# Print the total number of keys in the word2idx dictionary.
print('Total number of tokens in vocabulary:', len(data_loader.dataset.vocab))
# There are also a few special keys in the `word2idx` dictionary. Other than the special start word (`"<start>"`) and special end word (`"<end>"`), there is one more special token, corresponding to unknown words (`"<unk>"`). All tokens that don't appear anywhere in the `word2idx` dictionary are considered unknown words. In the pre-processing step, any unknown tokens are mapped to the integer `2`.
# In[12]:
unk_word = data_loader.dataset.vocab.unk_word
print('Special unknown word:', unk_word)
print('All unknown words are mapped to this integer:', data_loader.dataset.vocab(unk_word))
print ("For example:")
print("'jfkafejw' is mapped to", data_loader.dataset.vocab('jfkafejw'))
# The final thing to mention is the `vocab_from_file` argument that is supplied when creating a data loader. When we create a new data loader, the vocabulary (`data_loader.dataset.vocab`) is saved as a [pickle](https://docs.python.org/3/library/pickle.html) file in the project folder, with filename `vocab.pkl`.
#
# If we are still tweaking the value of the `vocab_threshold` argument, we **must** set `vocab_from_file=False` to have our changes take effect.
#
# But once we are happy with the value that we have chosen for the `vocab_threshold` argument, we need only run the data loader *one more time* with our chosen `vocab_threshold` to save the new vocabulary to file. Then, we can henceforth set `vocab_from_file=True` to load the vocabulary from file and speed the instantiation of the data loader. Note that building the vocabulary from scratch is the most time-consuming part of instantiating the data loader, and so we are strongly encouraged to set `vocab_from_file=True` as soon as we are able.
#
# Note that if `vocab_from_file=True`, then any supplied argument for `vocab_threshold` when instantiating the data loader is completely ignored.
# In[13]:
# Obtain the data loader (from file). Note that it runs much faster than before!
data_loader = get_loader(transform=transform_train,
mode='train',
batch_size=batch_size,
vocab_from_file=True)
# <a id='step2'></a>
# ## Step 2: Use the Data Loader to Obtain Batches
#
# The captions in the dataset vary greatly in length. We can see this by examining `data_loader.dataset.caption_lengths`, a Python list with one entry for each training caption (where the value stores the length of the corresponding caption).
#
# In the code cell below, we use this list to print the total number of captions in the training data with each length. As we will see below, the majority of captions have length 10. Likewise, very short and very long captions are quite rare.
# In[14]:
from collections import Counter
# Tally the total number of training captions with each length.
counter = Counter(data_loader.dataset.caption_lengths)
lengths = sorted(counter.items(), key=lambda pair: pair[1], reverse=True)
for value, count in lengths:
print('value: %2d --- count: %5d' % (value, count))
# To generate batches of training data, we begin by first sampling a caption length (where _the probability that any length is drawn is proportional to the number of captions with that length_ in the dataset). Then, we retrieve a batch of size `batch_size` of image-caption pairs, where all captions have the sampled length. This approach for assembling batches matches the procedure in [this paper](https://arxiv.org/pdf/1502.03044.pdf) and has been shown to be computationally efficient without degrading performance.
#
# Run the code cell below to generate a batch. The `get_indices` method in the `CoCoDataset` class first samples a caption length, and then samples `batch_size` indices corresponding to training data points with captions of that length. These indices are stored below in `indices`.
#
# These indices are supplied to the data loader, which then is used to retrieve the corresponding data points. The pre-processed images and captions in the batch are stored in `images` and `captions`.
# In[15]:
import numpy as np
import torch.utils.data as data
# Randomly sample a caption length, and sample indices with that length.
indices = data_loader.dataset.get_indices()
print('{} sampled indices: {}'.format(len(indices), indices))
# Create and assign a batch sampler to retrieve a batch with the sampled indices.
new_sampler = data.sampler.SubsetRandomSampler(indices=indices)
data_loader.batch_sampler.sampler = new_sampler
# Obtain the batch.
for batch in data_loader:
images, captions = batch[0], batch[1]
break
print('images.shape:', images.shape)
print('captions.shape:', captions.shape)
# Print the pre-processed images and captions.
#print('images:', images)
#print('captions:', captions)
# <a id='step3'></a>
# ## Step 3: Experiment with the CNN Encoder
#
# First, we will import `EncoderCNN` and `DecoderRNN` from **model.py**.
# In[16]:
# Import EncoderCNN and DecoderRNN.
from model import EncoderCNN, DecoderRNN
# Now we will instantiate the CNN encoder in `encoder`.
#
# The pre-processed images from the batch in **Step 2** of this notebook are then passed through the encoder, and the output is stored in `features`. The assert statement ensures that `features` has shape `[batch_size, embed_size]`.
# In[17]:
# Specify the dimensionality of the image embedding.
embed_size = 256
# Initialize the encoder. (We can add additional arguments if necessary.)
encoder = EncoderCNN(embed_size)
# Move the encoder to GPU if CUDA is available.
if torch.cuda.is_available():
encoder = encoder.cuda()
# Move the last batch of images from Step 2 to GPU if CUDA is available
if torch.cuda.is_available():
images = images.cuda()
# Pass the images through the encoder.
features = encoder(images)
print('type(features):', type(features))
print('features.shape:', features.shape)
# Check that our encoder satisfies some requirements of the project!
assert (features.shape[0]==batch_size) & (features.shape[1]==embed_size), "The shape of the encoder output is incorrect."
# This encoder uses the pre-trained ResNet-50 architecture (with the final fully-connected layer removed) to extract features from a batch of pre-processed images. The output is then flattened to a vector, before being passed through a `Linear` layer to transform the feature vector to have the same size as the word embedding.
#
# 
#
# We could amend the encoder in **model.py**, to experiment with other architectures, such as using a [different pre-trained model architecture](http://pytorch.org/docs/master/torchvision/models.html) or [adding batch normalization](http://pytorch.org/docs/master/nn.html#normalization-layers).
#
# For this project, we will **incorporate a pre-trained CNN into our encoder**. The `EncoderCNN` class must take `embed_size` as an input argument, which will also correspond to the dimensionality of the input to the RNN decoder that we will implement in Step 4. When we train our model in the next notebook in this sequence (**2_Training.ipynb**), we will tweak the value of `embed_size`.
# <a id='step4'></a>
# ## Step 4: Implement the RNN Decoder
#
# Our decoder will be an instance of the `DecoderRNN` class from **model.py** and must accept as input:
# - the PyTorch tensor `features` containing the embedded image features (outputted in Step 3, when the last batch of images from Step 2 was passed through `encoder`), along with
# - a PyTorch tensor corresponding to the last batch of captions (`captions`) from Step 2.
#
# Every training batch will contain pre-processed captions where all have the same length (`captions.shape[1]`), so **we won't need to worry about padding**.
#
# Although we will test the decoder using the last batch that is currently stored in the notebook, our decoder should accept an arbitrary batch (of embedded image features and pre-processed captions [where all captions have the same length]) as input.
#
# 
#
# In the code cell below, `outputs` should have size `[batch_size, captions.shape[1], vocab_size]`. Our output should be designed such that `outputs[i,j,k]` contains the model's predicted score, indicating how likely the `j`-th token in the `i`-th caption in the batch is the `k`-th token in the vocabulary. In the next notebook of the sequence (**2_Training.ipynb**), we will supply these scores to the [`torch.nn.CrossEntropyLoss`](http://pytorch.org/docs/master/nn.html#torch.nn.CrossEntropyLoss) optimizer in PyTorch.
# In[18]:
# Specify the number of features in the hidden state of the RNN decoder.
hidden_size = 512
# Store the size of the vocabulary.
vocab_size = len(data_loader.dataset.vocab)
# Initialize the decoder.
decoder = DecoderRNN(embed_size, hidden_size, vocab_size)
# Move the decoder to GPU if CUDA is available.
if torch.cuda.is_available():
decoder = decoder.cuda()
# Move the last batch of captions (from Step 1) to GPU if cuda is availble
if torch.cuda.is_available():
captions = captions.cuda()
# Pass the encoder output and captions through the decoder
outputs = decoder(features, captions)
print('type(outputs):', type(outputs))
print('outputs.shape:', outputs.shape)
# Check that our decoder satisfies some requirements of the project!
assert (outputs.shape[0]==batch_size) & (outputs.shape[1]==captions.shape[1]) & (outputs.shape[2]==vocab_size), "The shape of the decoder output is incorrect."
############### training ###############
#!/usr/bin/env python
# coding: utf-8
# # Image Captioning
#
# ## Part 2: Train a CNN-RNN Model
#
# ---
#
# In this notebook, we will train our CNN-RNN model.
#
# - [Step 1](#step1): Training Setup
# - [1a](#1a): CNN-RNN architecture
# - [1b](#1b): Hyperparameters and other variables
# - [1c](#1c): Image transform
# - [1d](#1d): Data loader
# - [1e](#1e): Loss function, learnable parameters and optimizer
#
#
# - [Step 2](#step2): Train and Validate the Model
# - [2a](#2a): Train for the first time
# - [2b](#2b): Resume training
# - [2c](#2c): Validation
# - [2d](#2d): Notes regarding model validation
# <a id='step1'></a>
# ## Step 1: Training Setup
#
# We will describe the model architecture and specify hyperparameters and set other options that are important to the training procedure. We will refer to [this paper](https://arxiv.org/pdf/1502.03044.pdf) and [this paper](https://arxiv.org/pdf/1411.4555.pdf) for useful guidance.
#
# <a id='1a'></a>
# ### CNN-RNN architecture
#
# For the complete CNN-RNN model, see **model.py**.
#
# - For the encoder model, we use a pre-trained ResNet which has been known to achieve great success in image classification. We use batch normalization because according to [this paper](https://arxiv.org/abs/1502.03167) it "allows us to use much higher learning rates and be less careful about initialization. It also acts as a regularizer, in some cases eliminating the need for Dropout."
# - The decoder is an RNN which has an Embedding layer, a LSTM layer and a fully-connected layer. LSTM has been shown to be successful in sequence generation.
#
# <a id='1b'></a>
# ### Hyperparameters and other variables
#
# In the next code cell, we will set the values for:
#
# - `batch_size` - the batch size of each training batch. It is the number of image-caption pairs used to amend the model weights in each training step. We will set it to `32`.
# - `vocab_threshold` - the minimum word count threshold. A larger threshold will result in a smaller vocabulary, whereas a smaller threshold will include rarer words and result in a larger vocabulary. We will set it to `5` just like [this paper](https://arxiv.org/pdf/1411.4555.pdf)
# - `vocab_from_file` - a Boolean that decides whether to load the vocabulary from file. This will be changed to `True` once we are done setting `vocab_threshold` and generating a `vocab.pkl` file.
# - `embed_size` - the dimensionality of the image and word embeddings. We have tried `512` as done in [this paper](https://arxiv.org/pdf/1411.4555.pdf) but it took a long time to train, so I will set it to `256`.
# - `hidden_size` - the number of features in the hidden state of the RNN decoder. We will use `512` based on [this paper](https://arxiv.org/pdf/1411.4555.pdf). The larger the number, the better the RNN model can memorize sequences. However, larger numbers can significantly slow down the training process.
# - `num_epochs` - the number of epochs to train the model. We are dealing with a huge amount of data so it will take a long time to complete even 1 epoch. Therefore, we will set `num_epochs` to `1`. We will save the model AND the optimizer every 100 training steps, and to resume training from the last step.
# In[1]:
# Watch for any changes in vocabulary.py, data_loader.py, utils.py or model.py, and re-load it automatically.
#get_ipython().run_line_magic('load_ext', 'autoreload')
#get_ipython().run_line_magic('autoreload', '2')
# In[2]:
import torch
import torch.nn as nn
from torch.autograd import Variable
from torchvision import transforms
import sys
from pycocotools.coco import COCO
import math
import torch.utils.data as data
import numpy as np
import os
import requests
import time
from utils import train, validate, save_epoch, early_stopping
from data_loader import get_loader
from model import EncoderCNN, DecoderRNN
# Set values for the training variables
batch_size = 32 # batch size
vocab_threshold = 5 # minimum word count threshold
vocab_from_file = True # if True, load existing vocab file
embed_size = 256 # dimensionality of image and word embeddings
hidden_size = 512 # number of features in hidden state of the RNN decoder
num_epochs = 50 # number of training epochs
# <a id='1c'></a>
# ### Image transform
#
# When setting this transform, we keep two things in mind:
# - the images in the dataset have varying heights and widths, and
# - since we are using a pre-trained model, we must perform the corresponding appropriate normalization.
#
# **Training set**: As seen in the following code cell, we will set the transform for training set as follows:
#
# ```python
# transform_train = transforms.Compose([
# transforms.Resize(256), # smaller edge of image resized to 256
# transforms.RandomCrop(224), # get 224x224 crop from random location
# transforms.RandomHorizontalFlip(), # horizontally flip image with probability=0.5
# transforms.ToTensor(), # convert the PIL Image to a tensor
# transforms.Normalize((0.485, 0.456, 0.406), # normalize image for pre-trained model
# (0.229, 0.224, 0.225))])
# ```
#
# According to [this page](https://pytorch.org/docs/master/torchvision/models.html), like other pre-trained models, ResNet expects input images normalized as follows:
# - The images are expected to have width and height of at least 224. The first and second transformations resize and crop the images to 224 x 224:
# ```python
# transforms.Resize(256), # smaller edge of image resized to 256
# transforms.RandomCrop(224), # get 224x224 crop from random location
# ```
# - The images have to be converted from numpy.ndarray (H x W x C) in the range [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0]:
# ```python
# transforms.ToTensor(), # convert the PIL Image to a tensor
# ```
# - Then they are normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225]. This is achieved using the last transformation step:
# ```python
# transforms.Normalize((0.485, 0.456, 0.406), # normalize image for pre-trained model
# (0.229, 0.224, 0.225))
# ```
#
# The data augmentation step `transforms.RandomHorizontalFlip()` improves the accuracy of the image classification task as mentioned in [this paper](http://cs231n.stanford.edu/reports/2017/pdfs/300.pdf).
#
# **Validation set**: We won't use the image augmentation step, i.e. RandomHorizontalFlip(), and will use CenterCrop() instead of RandomCrop().
# In[3]:
# Define a transform to pre-process the training images
transform_train = transforms.Compose([
transforms.Resize(256), # smaller edge of image resized to 256
transforms.RandomCrop(224), # get 224x224 crop from random location
transforms.RandomHorizontalFlip(), # horizontally flip image with probability=0.5
transforms.ToTensor(), # convert the PIL Image to a tensor
transforms.Normalize((0.485, 0.456, 0.406), # normalize image for pre-trained model
(0.229, 0.224, 0.225))])
# Define a transform to pre-process the validation images
transform_val = transforms.Compose([
transforms.Resize(256), # smaller edge of image resized to 256
transforms.CenterCrop(224), # get 224x224 crop from the center
transforms.ToTensor(), # convert the PIL Image to a tensor
transforms.Normalize((0.485, 0.456, 0.406), # normalize image for pre-trained model
(0.229, 0.224, 0.225))])
# <a id='1d'></a>
# ### Data loader
# We will build data loaders for training and validation sets, applying the above image transforms. We will then get the size of the vocabulary from the `train_loader`, and use it to initialize our `encoder` and `decoder`.
# In[4]:
# Build data loader, applying the transforms
train_loader = get_loader(transform=transform_train,
mode='train',
batch_size=batch_size,
vocab_threshold=vocab_threshold,
vocab_from_file=vocab_from_file)
val_loader = get_loader(transform=transform_val,
mode='val',
batch_size=batch_size,
vocab_threshold=vocab_threshold,
vocab_from_file=vocab_from_file)
# The size of the vocabulary
vocab_size = len(train_loader.dataset.vocab)
# Initialize the encoder and decoder
encoder = EncoderCNN(embed_size)
decoder = DecoderRNN(embed_size, hidden_size, vocab_size)
# Move models to GPU if CUDA is available
if torch.cuda.is_available():
encoder.cuda()
decoder.cuda()
# <a id='1e'></a>
# ### Loss function, learnable parameters and optimizer
#
# **Loss function**: We will use `CrossEntropyLoss()`.
#
# **Learnable parameters**: According to [this paper](https://arxiv.org/pdf/1411.4555.pdf), the "loss is minimized w.r.t. all the parameters of the LSTM, the top layer of the image embedder CNN and word embeddings." We will follow this strategy and choose the parameters accordingly. Since we also added a Batch Normalization layer, we will optimize its parameters too. This makes sense for two reasons:
# - the EncoderCNN in this project uses ResNet which has been pre-trained on an image classification task. So we don't have to optimize the parameters of the entire network again for a similar image classification task. We only need to optimize the top layer whose outputs are fed into the DecoderRNN.
# - the DecoderRNN is not a pre-trained network, so we have to optimize all its parameters.
#
# **Optimizer**: According to [this paper](https://arxiv.org/pdf/1502.03044.pdf), Adam optimizer works best on the MS COCO Dataset. Therefore, we will use it.
# In[5]:
# Define the loss function
criterion = nn.CrossEntropyLoss().cuda() if torch.cuda.is_available() else nn.CrossEntropyLoss()
# Specify the learnable parameters of the model
params = list(decoder.parameters()) + list(encoder.embed.parameters()) + list(encoder.bn.parameters())
# Define the optimizer
optimizer = torch.optim.Adam(params=params, lr=0.001)
# <a id='step2'></a>
# ## Step 2: Train and Validate the Model
#
# At the beginning of this notebook, we have imported the `train` fuction and the `validate` function from `utils.py`. To figure out how well our model is doing, we will print out the training loss and perplexity during training. We will try to minimize overfitting by assessing the model's performance, i.e. the Bleu-4 score, on the validation dataset.
#
# It will take a long time to train and validate the model. Therefore we will split the training procedure into two parts: first, we will train the model for the first time and save the it every 100 steps; then we will resume, as many times as we would like or until the early stopping criterion is satisfied. We will save the model and optimizer weights in the `models` subdirectory. We will do the same for the validation procedure.
#
# First, let's calculate the total number of training and validation steps per epoch.
# In[6]:
# Set the total number of training and validation steps per epoch
total_train_step = math.ceil(len(train_loader.dataset.caption_lengths) / train_loader.batch_sampler.batch_size)
total_val_step = math.ceil(len(val_loader.dataset.caption_lengths) / val_loader.batch_sampler.batch_size)
print ("Number of training steps:", total_train_step)
print ("Number of validation steps:", total_val_step)
# <a id='2a'></a>
# ### Train for the first time
#
# Run the below cell if training for the first time or training continously without break. To resume training, skip this cell and run the one below it.
# In[ ]:
# Keep track of train and validation losses and validation Bleu-4 scores by epoch
train_losses = []
val_losses = []
val_bleus = []
# Keep track of the current best validation Bleu score
best_val_bleu = float("-INF")
start_time = time.time()
for epoch in range(1, num_epochs + 1):
train_loss = train(train_loader, encoder, decoder, criterion, optimizer,
vocab_size, epoch, total_train_step)
train_losses.append(train_loss)
val_loss, val_bleu = validate(val_loader, encoder, decoder, criterion,
train_loader.dataset.vocab, epoch, total_val_step)
val_losses.append(val_loss)
val_bleus.append(val_bleu)
if val_bleu > best_val_bleu:
print ("Validation Bleu-4 improved from {:0.4f} to {:0.4f}, saving model to best-model.pkl".
format(best_val_bleu, val_bleu))
best_val_bleu = val_bleu
filename = os.path.join("./models", "best-model.pkl")
save_epoch(filename, encoder, decoder, optimizer, train_losses, val_losses,
val_bleu, val_bleus, epoch)
else:
print ("Validation Bleu-4 did not improve, saving model to model-{}.pkl".format(epoch))
# Save the entire model anyway, regardless of being the best model so far or not
filename = os.path.join("./models", "model-{}.pkl".format(epoch))
save_epoch(filename, encoder, decoder, optimizer, train_losses, val_losses,
val_bleu, val_bleus, epoch)
print ("Epoch [%d/%d] took %ds" % (epoch, num_epochs, time.time() - start_time))
if epoch > 5:
# Stop if the validation Bleu doesn't improve for 3 epochs
if early_stopping(val_bleus, 3):
break
start_time = time.time()
# <a id='2b'></a>
# ### Resume training
#
# Resume training if having trained and saved the model. There are two types of data loading for training depending on where we are in the process:
# 1. We will load a model from the latest training step if we are in the middle of the process and have previously saved a model, e.g. train-model-14000.pkl which means model was saved for epoch 1 at training step 4000.
# 2. We will load a model saved by the below validation process after completing validating one epoch. This is when we start to train the next epoch. Therefore, we need to reset `start_loss` and `start_step` to 0.0 and 1 respectively.
#
# We will modify the code cell below depending on where we are in the training process.
# In[ ]:
'''
# Load the last checkpoints
checkpoint = torch.load(os.path.join('./models', 'train-model-76500.pkl'))
# Load the pre-trained weights
encoder.load_state_dict(checkpoint['encoder'])
decoder.load_state_dict(checkpoint['decoder'])
optimizer.load_state_dict(checkpoint['optimizer'])
# Load start_loss from checkpoint if in the middle of training process; otherwise, comment it out
start_loss = checkpoint['total_loss']
# Reset start_loss to 0.0 if starting a new epoch; otherwise comment it out
#start_loss = 0.0
# Load epoch. Add 1 if we start a new epoch
epoch = checkpoint['epoch']
# Load start_step from checkpoint if in the middle of training process; otherwise, comment it out
start_step = checkpoint['train_step'] + 1
# Reset start_step to 1 if starting a new epoch; otherwise comment it out
#start_step = 1
# Train 1 epoch at a time due to very long training time
train_loss = train(train_loader, encoder, decoder, criterion, optimizer,
vocab_size, epoch, total_train_step, start_step, start_loss)
'''
# Now that we have completed training an entire epoch, we will save the necessary information. We will load pre-trained weights from the last train step `train-model-{epoch}12900.pkl`, `best_val_bleu` from `best-model.pkl` and the rest from `model-{epoch}.pkl`). We will append `train_loss` to the list `train_losses`. Then we will save the information needed for the epoch.
# In[8]:
'''
# Load checkpoints
train_checkpoint = torch.load(os.path.join('./models', 'train-model-712900.pkl'))
epoch_checkpoint = torch.load(os.path.join('./models', 'model-6.pkl'))
best_checkpoint = torch.load(os.path.join('./models', 'best-model.pkl'))
# Load the pre-trained weights and epoch from the last train step
encoder.load_state_dict(train_checkpoint['encoder'])
decoder.load_state_dict(train_checkpoint['decoder'])
optimizer.load_state_dict(train_checkpoint['optimizer'])
epoch = train_checkpoint['epoch']
# Load from the previous epoch
train_losses = epoch_checkpoint['train_losses']
val_losses = epoch_checkpoint['val_losses']
val_bleus = epoch_checkpoint['val_bleus']
# Load from the best model
best_val_bleu = best_checkpoint['val_bleu']
train_losses.append(train_loss)
print (train_losses, val_losses, val_bleus, best_val_bleu)
print ("Training completed for epoch {}, saving model to train-model-{}.pkl".format(epoch, epoch))
filename = os.path.join("./models", "train-model-{}.pkl".format(epoch))
save_epoch(filename, encoder, decoder, optimizer, train_losses, val_losses,
best_val_bleu, val_bleus, epoch)
# <a id='2c'></a>
# ### Validation
#
# We will do validation for an epoch once we have trained and saved the model for that epoch. There are two types of data loading for validation depending on where we are in the process:
# 1. We will load a model from the latest validation step if we are in the middle of the process and have previously saved a model, e.g. val-model-14000.pkl which means the model was saved for epoch 1 at val step 4000.
# 2. We will load a model saved by the above training process after completing training one epoch. This is when we just start to do validation, i.e. at validation step \#1. Therefore, we need to reset `start_loss`, `start_bleu` and `start_step` to 0.0, 0.0 and 1 respectively.
#
# We will modify the code cell below depending on where we are in the validation process.
# In[7]:
# Load the last checkpoint
checkpoint = torch.load(os.path.join('./models', 'val-model-75500.pkl'))
# Load the pre-trained weights
encoder.load_state_dict(checkpoint['encoder'])
decoder.load_state_dict(checkpoint['decoder'])
# Load these from checkpoint if in the middle of validation process; otherwise, comment them out
start_loss = checkpoint['total_loss']
start_bleu = checkpoint['total_bleu_4']
# Reset these to 0.0 if starting validation for an epoch; otherwise comment them out
#start_loss = 0.0
#start_bleu = 0.0
# Load epoch
epoch = checkpoint['epoch']
# Load start_step from checkpoint if in the middle of training process; otherwise, comment it out
start_step = checkpoint['val_step'] + 1
# Reset start_step to 1 if starting a new epoch; otherwise comment it out
#start_step = 1
# Validate 1 epoch at a time due to very long validation time
val_loss, val_bleu = validate(val_loader, encoder, decoder, criterion,
train_loader.dataset.vocab, epoch, total_val_step,
start_step, start_loss, start_bleu)
# Now that we have completed training and validation for an entire epoch, we will save all the necessary information. We will load most information from `train-model-{epoch}.pkl` and `best_val_bleu` from `best-model.pkl`. We will then do the following updates:
# - appending `val_bleu` and `val_loss` to the lists `val_bleus` and `val_losses` respectively
# - updating `best_val_bleu` if it is not as good as `val_bleu` we just got in the above cell
#
# Then we will save the information needed for the epoch.
# In[8]:
# Load checkpoints
checkpoint = torch.load(os.path.join('./models', 'train-model-7.pkl'))
best_checkpoint = torch.load(os.path.join('./models', 'best-model.pkl'))
# Load the pre-trained weights
encoder.load_state_dict(checkpoint['encoder'])
decoder.load_state_dict(checkpoint['decoder'])
optimizer.load_state_dict(checkpoint['optimizer'])
# Load train and validation losses and validation Bleu-4 scores
train_losses = checkpoint['train_losses']
val_losses = checkpoint['val_losses']
val_bleus = checkpoint['val_bleus']
best_val_bleu = best_checkpoint['val_bleu']
# Load epoch
epoch = checkpoint['epoch']
val_losses.append(val_loss)
val_bleus.append(val_bleu)
print (train_losses, val_losses, val_bleus, best_val_bleu)
if val_bleu > best_val_bleu:
print ("Validation Bleu-4 improved from {:0.4f} to {:0.4f}, saving model to best-model.pkl".
format(best_val_bleu, val_bleu))
best_val_bleu = val_bleu
print (best_val_bleu)
filename = os.path.join("./models", "best-model.pkl")
save_epoch(filename, encoder, decoder, optimizer, train_losses, val_losses,
val_bleu, val_bleus, epoch)
else:
print ("Validation Bleu-4 did not improve, saving model to model-{}.pkl".format(epoch))
# Save the entire model anyway, regardless of being the best model so far or not
filename = os.path.join("./models", "model-{}.pkl".format(epoch))
save_epoch(filename, encoder, decoder, optimizer, train_losses, val_losses,
val_bleu, val_bleus, epoch)
if epoch > 5:
# Stop if the validation Bleu doesn't improve for 3 epochs
if early_stopping(val_bleus, 3):
print ("Val Bleu-4 doesn't improve anymore. Early stopping")
# <a id='2d'></a>
# ### Notes regarding model validation
#
# - Another way to validate a model involves creating a json file such as [this one](https://github.com/cocodataset/cocoapi/blob/master/results/captions_val2014_fakecap_results.json) containing the model's predicted captions for the validation images. Then, write up a script or use one [available online](https://github.com/tylin/coco-caption) to calculate the BLEU score of the model.
# - Other evaluation metrics (such as TEOR and Cider) are mentioned in section 4.1 of [this paper](https://arxiv.org/pdf/1411.4555.pdf).
#
#
# # Next steps
#
# A few things that we may try in the future to improve model performance:
#
# - Adjust learning rate: make it decay over time, as in [this example](https://github.com/pytorch/examples/blob/master/imagenet/main.py).
# - Run the code on a GPU to so that we can train the model more. Tried AWS p2.xlarge; however, the datasets exceeded the storage limit.
'''
| [
"noreply@github.com"
] | noreply@github.com |
1f57e9f7d9a437bfa2ea01c025ce08e48b2b9f90 | 52dd3faf498e8d189a50bfda520ea79aa35a7e9b | /new_train.py | fd6cd4c2f2b3dc08a67f48fbd90374a169bd9a8e | [] | no_license | captainswain/Eye-fullerton | d3def35c15405f3b6f0bebd9fa1e93a95036ed12 | 2135c1f13e65fc7d693d43fa102f2d3c208c5260 | refs/heads/master | 2022-03-12T20:52:24.680293 | 2019-12-12T01:26:28 | 2019-12-12T01:26:28 | 225,145,277 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,370 | py | # EyeFullerton Model Training
# This code is modified from google tensorflows documentation found below:
# https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/tensorflow2_image_retraining.ipynb
import os
import tensorflow
import tensorflow_hub as hub
IMAGE_SIZE = (224, 224)
BATCH_SIZE = 32
# Directory containing dataset
data_dir = "/Users/slindsay/Documents/Code/Model-dataset-training/dataset"
# Args for flow_from directory and ImageDataGenerator
datagen_kwargs = dict(rescale=1./255, validation_split=.20)
dataflow_kwargs = dict(target_size=IMAGE_SIZE, batch_size=BATCH_SIZE,
interpolation="bilinear")
valid_datagen = tensorflow.keras.preprocessing.image.ImageDataGenerator(
**datagen_kwargs)
valid_generator = valid_datagen.flow_from_directory(
data_dir, subset="validation", shuffle=False, **dataflow_kwargs)
# Generate batches of tensor image data with real-time data augmentation.
train_datagen = tensorflow.keras.preprocessing.image.ImageDataGenerator(
rotation_range=40,
horizontal_flip=True,
width_shift_range=0.2, height_shift_range=0.2,
shear_range=0.2, zoom_range=0.2,
**datagen_kwargs)
model = tensorflow.keras.Sequential([
# Wrap mobilenet_v2 Hub modul as a Keras Layer.
hub.KerasLayer("https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4", trainable=True),
# Apply dropout to the layer to combat overfitting
tensorflow.keras.layers.Dropout(rate=0.2),
tensorflow.keras.layers.Dense(train_generator.num_classes, activation='softmax',
kernel_regularizer=tensorflow.keras.regularizers.l2(0.0001))
])
model.build((None,)+IMAGE_SIZE+(3,))
model.summary()
## training the model
model.compile(
optimizer=tensorflow.keras.optimizers.SGD(lr=0.005, momentum=0.9),
loss=tensorflow.keras.losses.CategoricalCrossentropy(label_smoothing=0.1),
metrics=['accuracy'])
steps_per_epoch = train_generator.samples // train_generator.batch_size
validation_steps = valid_generator.samples // valid_generator.batch_size
hist = model.fit_generator(
train_generator,
epochs=8, steps_per_epoch=steps_per_epoch,
validation_data=valid_generator,
validation_steps=validation_steps).history
# save model to model_new folder
tensorflow.saved_model.save(model, "./model_new/buildings_augmented")
| [
"me@shane.cx"
] | me@shane.cx |
782127dbf9d5162d6405d8212886c5fedbd07964 | e1d3f82d18c301f4d214fdd0157685232fcd02fd | /modules/get_deps.py | 20c2b2760b5ee25999285e7775b513d02ca487b9 | [
"MIT"
] | permissive | user062/Muzzle | 79a3acaf2d0a294a02bccf1212c4713011f4562d | 73f5e1d22f289201c63852b2ae9bef7bd2d20e77 | refs/heads/main | 2023-08-29T05:53:34.845503 | 2021-10-24T23:47:39 | 2021-10-24T23:47:39 | 414,215,223 | 0 | 0 | MIT | 2021-10-06T13:06:03 | 2021-10-06T13:06:02 | null | UTF-8 | Python | false | false | 511 | py | import subprocess
import platform
def main() -> None :
if (platform.system() == 'Windows'):
print(f"OS: Windows {platform.release()} {platform.version()}")
print("Compiling GLFW")
subprocess.call('cd ../deps/glfw/; mkdir build; cd build; cmake .. -G "MinGW Makefiles"; mingw32-make.exe')
else:
print(f"OS: {platform.system()}")
subprocess.call('cd ../deps/glfw/ && mkdir build && cd build && cmake .. && make')
if __name__ == "__main__":
main() | [
"probro1802@gmail.com"
] | probro1802@gmail.com |
0e28eda07956ac5acd87b7c0d705815ef5658849 | aa1972e6978d5f983c48578bdf3b51e311cb4396 | /nitro-python-1.0/nssrc/com/citrix/netscaler/nitro/resource/config/cs/csvserver_feopolicy_binding.py | acb178fbe6eab499c4161045696cebc885acad74 | [
"Python-2.0",
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | MayankTahil/nitro-ide | 3d7ddfd13ff6510d6709bdeaef37c187b9f22f38 | 50054929214a35a7bb19ed10c4905fffa37c3451 | refs/heads/master | 2020-12-03T02:27:03.672953 | 2017-07-05T18:09:09 | 2017-07-05T18:09:09 | 95,933,896 | 2 | 5 | null | 2017-07-05T16:51:29 | 2017-07-01T01:03:20 | HTML | UTF-8 | Python | false | false | 11,337 | py | #
# Copyright (c) 2008-2016 Citrix Systems, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License")
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from nssrc.com.citrix.netscaler.nitro.resource.base.base_resource import base_resource
from nssrc.com.citrix.netscaler.nitro.resource.base.base_resource import base_response
from nssrc.com.citrix.netscaler.nitro.service.options import options
from nssrc.com.citrix.netscaler.nitro.exception.nitro_exception import nitro_exception
from nssrc.com.citrix.netscaler.nitro.util.nitro_util import nitro_util
class csvserver_feopolicy_binding(base_resource) :
""" Binding class showing the feopolicy that can be bound to csvserver.
"""
def __init__(self) :
self._policyname = None
self._priority = None
self._gotopriorityexpression = None
self._bindpoint = None
self._name = None
self._targetlbvserver = None
self._invoke = None
self._labeltype = None
self._labelname = None
self.___count = 0
@property
def priority(self) :
r"""Priority for the policy.
"""
try :
return self._priority
except Exception as e:
raise e
@priority.setter
def priority(self, priority) :
r"""Priority for the policy.
"""
try :
self._priority = priority
except Exception as e:
raise e
@property
def bindpoint(self) :
r"""The bindpoint to which the policy is bound.<br/>Possible values = REQUEST, RESPONSE.
"""
try :
return self._bindpoint
except Exception as e:
raise e
@bindpoint.setter
def bindpoint(self, bindpoint) :
r"""The bindpoint to which the policy is bound.<br/>Possible values = REQUEST, RESPONSE
"""
try :
self._bindpoint = bindpoint
except Exception as e:
raise e
@property
def policyname(self) :
r"""Policies bound to this vserver.
"""
try :
return self._policyname
except Exception as e:
raise e
@policyname.setter
def policyname(self, policyname) :
r"""Policies bound to this vserver.
"""
try :
self._policyname = policyname
except Exception as e:
raise e
@property
def labelname(self) :
r"""Name of the label to be invoked.
"""
try :
return self._labelname
except Exception as e:
raise e
@labelname.setter
def labelname(self, labelname) :
r"""Name of the label to be invoked.
"""
try :
self._labelname = labelname
except Exception as e:
raise e
@property
def name(self) :
r"""Name of the content switching virtual server to which the content switching policy applies.<br/>Minimum length = 1.
"""
try :
return self._name
except Exception as e:
raise e
@name.setter
def name(self, name) :
r"""Name of the content switching virtual server to which the content switching policy applies.<br/>Minimum length = 1
"""
try :
self._name = name
except Exception as e:
raise e
@property
def targetlbvserver(self) :
r"""Name of the Load Balancing virtual server to which the content is switched, if policy rule is evaluated to be TRUE.
Example: bind cs vs cs1 -policyname pol1 -priority 101 -targetLBVserver lb1
Note: Use this parameter only in case of Content Switching policy bind operations to a CS vserver.
"""
try :
return self._targetlbvserver
except Exception as e:
raise e
@targetlbvserver.setter
def targetlbvserver(self, targetlbvserver) :
r"""Name of the Load Balancing virtual server to which the content is switched, if policy rule is evaluated to be TRUE.
Example: bind cs vs cs1 -policyname pol1 -priority 101 -targetLBVserver lb1
Note: Use this parameter only in case of Content Switching policy bind operations to a CS vserver
"""
try :
self._targetlbvserver = targetlbvserver
except Exception as e:
raise e
@property
def gotopriorityexpression(self) :
r"""Expression specifying the priority of the next policy which will get evaluated if the current policy rule evaluates to TRUE.
"""
try :
return self._gotopriorityexpression
except Exception as e:
raise e
@gotopriorityexpression.setter
def gotopriorityexpression(self, gotopriorityexpression) :
r"""Expression specifying the priority of the next policy which will get evaluated if the current policy rule evaluates to TRUE.
"""
try :
self._gotopriorityexpression = gotopriorityexpression
except Exception as e:
raise e
@property
def invoke(self) :
r"""Invoke a policy label if this policy's rule evaluates to TRUE (valid only for default-syntax policies such as application firewall, transform, integrated cache, rewrite, responder, and content switching).
"""
try :
return self._invoke
except Exception as e:
raise e
@invoke.setter
def invoke(self, invoke) :
r"""Invoke a policy label if this policy's rule evaluates to TRUE (valid only for default-syntax policies such as application firewall, transform, integrated cache, rewrite, responder, and content switching).
"""
try :
self._invoke = invoke
except Exception as e:
raise e
@property
def labeltype(self) :
r"""Type of label to be invoked.
"""
try :
return self._labeltype
except Exception as e:
raise e
@labeltype.setter
def labeltype(self, labeltype) :
r"""Type of label to be invoked.
"""
try :
self._labeltype = labeltype
except Exception as e:
raise e
def _get_nitro_response(self, service, response) :
r""" converts nitro response into object and returns the object array in case of get request.
"""
try :
result = service.payload_formatter.string_to_resource(csvserver_feopolicy_binding_response, response, self.__class__.__name__)
if(result.errorcode != 0) :
if (result.errorcode == 444) :
service.clear_session(self)
if result.severity :
if (result.severity == "ERROR") :
raise nitro_exception(result.errorcode, str(result.message), str(result.severity))
else :
raise nitro_exception(result.errorcode, str(result.message), str(result.severity))
return result.csvserver_feopolicy_binding
except Exception as e :
raise e
def _get_object_name(self) :
r""" Returns the value of object identifier argument
"""
try :
if self.name is not None :
return str(self.name)
return None
except Exception as e :
raise e
@classmethod
def add(cls, client, resource) :
try :
if resource and type(resource) is not list :
updateresource = csvserver_feopolicy_binding()
updateresource.name = resource.name
updateresource.policyname = resource.policyname
updateresource.targetlbvserver = resource.targetlbvserver
updateresource.priority = resource.priority
updateresource.gotopriorityexpression = resource.gotopriorityexpression
updateresource.bindpoint = resource.bindpoint
updateresource.invoke = resource.invoke
updateresource.labeltype = resource.labeltype
updateresource.labelname = resource.labelname
return updateresource.update_resource(client)
else :
if resource and len(resource) > 0 :
updateresources = [csvserver_feopolicy_binding() for _ in range(len(resource))]
for i in range(len(resource)) :
updateresources[i].name = resource[i].name
updateresources[i].policyname = resource[i].policyname
updateresources[i].targetlbvserver = resource[i].targetlbvserver
updateresources[i].priority = resource[i].priority
updateresources[i].gotopriorityexpression = resource[i].gotopriorityexpression
updateresources[i].bindpoint = resource[i].bindpoint
updateresources[i].invoke = resource[i].invoke
updateresources[i].labeltype = resource[i].labeltype
updateresources[i].labelname = resource[i].labelname
return cls.update_bulk_request(client, updateresources)
except Exception as e :
raise e
@classmethod
def delete(cls, client, resource) :
try :
if resource and type(resource) is not list :
deleteresource = csvserver_feopolicy_binding()
deleteresource.name = resource.name
deleteresource.policyname = resource.policyname
deleteresource.bindpoint = resource.bindpoint
deleteresource.priority = resource.priority
return deleteresource.delete_resource(client)
else :
if resource and len(resource) > 0 :
deleteresources = [csvserver_feopolicy_binding() for _ in range(len(resource))]
for i in range(len(resource)) :
deleteresources[i].name = resource[i].name
deleteresources[i].policyname = resource[i].policyname
deleteresources[i].bindpoint = resource[i].bindpoint
deleteresources[i].priority = resource[i].priority
return cls.delete_bulk_request(client, deleteresources)
except Exception as e :
raise e
@classmethod
def get(cls, service, name="", option_="") :
r""" Use this API to fetch csvserver_feopolicy_binding resources.
"""
try :
if not name :
obj = csvserver_feopolicy_binding()
response = obj.get_resources(service, option_)
else :
obj = csvserver_feopolicy_binding()
obj.name = name
response = obj.get_resources(service)
return response
except Exception as e:
raise e
@classmethod
def get_filtered(cls, service, name, filter_) :
r""" Use this API to fetch filtered set of csvserver_feopolicy_binding resources.
Filter string should be in JSON format.eg: "port:80,servicetype:HTTP".
"""
try :
obj = csvserver_feopolicy_binding()
obj.name = name
option_ = options()
option_.filter = filter_
response = obj.getfiltered(service, option_)
return response
except Exception as e:
raise e
@classmethod
def count(cls, service, name) :
r""" Use this API to count csvserver_feopolicy_binding resources configued on NetScaler.
"""
try :
obj = csvserver_feopolicy_binding()
obj.name = name
option_ = options()
option_.count = True
response = obj.get_resources(service, option_)
if response :
return response[0].__dict__['___count']
return 0
except Exception as e:
raise e
@classmethod
def count_filtered(cls, service, name, filter_) :
r""" Use this API to count the filtered set of csvserver_feopolicy_binding resources.
Filter string should be in JSON format.eg: "port:80,servicetype:HTTP".
"""
try :
obj = csvserver_feopolicy_binding()
obj.name = name
option_ = options()
option_.count = True
option_.filter = filter_
response = obj.getfiltered(service, option_)
if response :
return response[0].__dict__['___count']
return 0
except Exception as e:
raise e
class Bindpoint:
REQUEST = "REQUEST"
RESPONSE = "RESPONSE"
class Labeltype:
reqvserver = "reqvserver"
resvserver = "resvserver"
policylabel = "policylabel"
class csvserver_feopolicy_binding_response(base_response) :
def __init__(self, length=1) :
self.csvserver_feopolicy_binding = []
self.errorcode = 0
self.message = ""
self.severity = ""
self.sessionid = ""
self.csvserver_feopolicy_binding = [csvserver_feopolicy_binding() for _ in range(length)]
| [
"Mayank@Mandelbrot.local"
] | Mayank@Mandelbrot.local |
2c4a1035039cc0c453786d797c264d0e98487a12 | 1259a0292d817afa3ee0064d94f7ad6c340281e8 | /game/views.py | 95b4e0317a0a7931019f143e418c0ebf55005170 | [] | no_license | stsummers95/JParty | 2a7a9b85f8251c6b369c02b21e33733395f6a154 | b8a83ea924f8aec221c0a9753b3169b245f8d952 | refs/heads/master | 2020-09-27T22:42:40.047856 | 2020-01-19T00:00:57 | 2020-01-19T00:00:57 | 226,627,352 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 929 | py | from django.shortcuts import get_object_or_404, render
from .models import Clues
import json
from django.core import serializers
from django.core.serializers.json import DjangoJSONEncoder
def index(request):
latest_clue_list = Clues.objects.order_by('-season', '-episode', 'clue_id')[:5]
context = { 'latest_clue_list': latest_clue_list, }
return render(request, 'game/index.html', context)
season_info = Clues.objects.distinct('season')
season_list = []
for val in season_info:
season_list.append(val.season)
context = { 'season_list': season_list }
def detail(request, episode):
check = get_object_or_404(Clues, episode=episode, clue_id=1)
episode = Clues.objects.filter(episode=episode)
episode_json = serializers.serialize('json', list(episode), cls=DjangoJSONEncoder)
return render(request, 'game/detail.html', {'episode': episode, 'episode_json': episode_json})
| [
"stephenleesummers@gmail.com"
] | stephenleesummers@gmail.com |
9cce36bd3cc2a944f704b616c6d7ddfa24126057 | 9cbe74fe073a6a22ae9e0543e12ad493dcf58a64 | /modules/enumerator.py | 98db2dedbd27d9056100a818716eaa83c3e40a10 | [] | no_license | sulaimanzai/kenzer | 77245cded4bf10296347621a21568c5bf39ad7a2 | 292991d77db2b2d4dfab3baa34c0e0e206f28819 | refs/heads/master | 2023-02-13T20:35:04.223858 | 2020-12-26T13:12:49 | 2020-12-26T13:12:49 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 10,735 | py | #imports
import os
#enumerator
class Enumerator:
#initializations
def __init__(self, domain, db, kenzer, github=""):
self.domain = domain
self.organization = domain
self.path = db+self.organization
self.resources = kenzer+"resources"
self.githubapi=github
if(os.path.exists(self.path) == False):
os.system("mkdir "+self.path)
#core enumerator modules
#enumerates subdomains
def subenum(self):
self.gitdomain()
self.subfinder()
self.shuffledns()
domain = self.domain
path = self.path
output =path+"/subenum.kenz"
if(os.path.exists(output)):
self.shuffsolv(output, domain)
os.system("rm {0}".format(output))
os.system("cat {0}/subfinder.log {0}/subenum.kenz* {0}/shuffledns.log {0}/shuffsolv.log {0}/gitdomain.log | sort -u > {1}".format(path, output))
if(os.path.exists(output)):
with open(output) as f:
line = len(f.readlines())
else:
line = 0
return line
#enumerates webservers
def webenum(self):
domain = self.domain
path = self.path
subs = path+"/portenum.kenz"
if(os.path.exists(subs) == False):
return("!portenum")
output = path+"/httpx.log"
if(os.path.exists(output)):
os.system("rm {0}".format(output))
self.httpx(subs, output)
output = path+"/webenum.kenz"
if(os.path.exists(output)):
os.system("mv {0} {0}.old".format(output))
os.system("cat {0}/httpx.log {0}/webenum.kenz* | cut -d' ' -f 1 | sort -u > {1}".format(path, output))
if(os.path.exists(output)):
with open(output) as f:
line = len(f.readlines())
else:
line = 0
return line
#enumerates additional information for webservers
def headenum(self):
domain = self.domain
path = self.path
subs = path+"/webenum.kenz"
if(os.path.exists(subs) == False):
return("!webenum")
output = path+"/headenum.kenz"
if(os.path.exists(output)):
os.system("rm {0}".format(output))
extras = " -status-code -title -web-server -websocket "
self.httpx(subs, output, extras)
if(os.path.exists(output)):
with open(output) as f:
line = len(f.readlines())
else:
line = 0
return line
#enumerates urls
def urlenum(self):
self.gau()
self.giturl()
domain = self.domain
path = self.path
output = path+"/urlenum.kenz"
if(os.path.exists(output)):
os.system("mv {0} {0}.old".format(output))
os.system("cat {0}/urlenum.kenz* {0}/gttpx* {0}/gittpx* | grep '\[200\]' | cut -d' ' -f 1 | sort -u> {1}".format(path, output))
if(os.path.exists(output)):
with open(output) as f:
line = len(f.readlines())
else:
line = 0
return line
#enumerates open ports using NXScan
def portenum(self):
domain = self.domain
path = self.path
subs = path+"/subenum.kenz"
if(os.path.exists(subs) == False):
return("!subenum")
self.shuffsolv(subs, domain)
output = path+"/portenum.kenz"
subs = path+"/shuffsolv.log"
if(os.path.exists(output)):
os.system("mv {0} {0}.old".format(output))
os.system("sudo NXScan --only-enumerate -l {0} -o {1}".format(subs,path+"/nxscan"))
os.system("cat {0}/nxscan/enum.txt {0}/portenum.kenz* | sort -u > {1}".format(path, output))
if(os.path.exists(output)):
with open(output) as f:
line = len(f.readlines())
else:
line = 0
return line
#enumerates dns records using DNSX
def dnsenum(self):
domain = self.domain
path = self.path
subs = path+"/subenum.kenz"
if(os.path.exists(subs) == False):
return("!subenum")
output = path+"/dnsenum.kenz"
if(os.path.exists(output)):
os.system("mv {0} {0}.old".format(output))
os.system("dnsx -l {0} -o {1} -a -aaaa -cname -mx -ptr -soa -txt -resp -retry 2".format(subs, output))
if(os.path.exists(output)):
with open(output) as f:
line = len(f.readlines())
else:
line = 0
return line
#enumerates asn using domlock
def asnenum(self):
domain = self.domain
path = self.path
subs = path+"/subenum.kenz"
if(os.path.exists(subs) == False):
return("!subenum")
output = path+"/asnenum.kenz"
if(os.path.exists(output)):
os.system("rm {0}".format(output))
os.system("domlock -l {0} -o {1}".format(subs, output))
if(os.path.exists(output)):
with open(output) as f:
line = len(f.readlines())
else:
line = 0
return line
#enumerates hidden files & directories using ffuf
def conenum(self):
domain = self.domain
path = self.path
subs = path+"/webenum.kenz"
if(os.path.exists(subs) == False):
return("!webenum")
output = path+"/conenum.kenz"
if(os.path.exists(output)):
os.system("rm {0}".format(output))
os.system("ffuf -u FuZZDoM/FuZZCoN -w {0}:FuZZDoM,{1}:FuZZCoN -mc 200 -of html -o {2} -t 80".format(subs, self.resources+"/kenzer-templates/ffuf.lst", output))
if(os.path.exists(output)):
with open(output) as f:
line = len(f.readlines())
else:
line = 0
return line
#helper modules
#downloads fresh list of public resolvers
def getresolvers(self):
output = self.resources+"/resolvers.txt"
if(os.path.exists(output)):
os.system("rm {0}".format(output))
os.system("wget -q https://public-dns.info/nameservers.txt -O {0}".format(output))
def generateSubdomainsWordist(self):
os.system("cd {0} && wget -q https://raw.githubusercontent.com/internetwache/CT_subdomains/master/top-100000.txt -O top-100000.txt".format(self.resources))
os.system("cd {0} && wget -q https://raw.githubusercontent.com/cqsd/daily-commonspeak2/master/wordlists/subdomains.txt -O subsB.txt".format(self.resources))
output = self.resources+"/subsA.txt"
os.system("cat {0}/top-100000.txt | cut -d ',' -f 2 | sort -u > {1}".format(self.resources, output))
output = self.resources+"/subdomains.txt"
os.system("cat {0}/subsA.txt {0}/subsB.txt | sort -u > {1}".format(self.resources, output))
#resolves & removes wildcard subdomains using shuffledns
def shuffsolv(self, domains, domain):
self.getresolvers()
path=self.path
path+="/shuffsolv.log"
if(os.path.exists(path)):
os.system("rm {0}".format(path))
os.system("shuffledns -strict-wildcard -retries 10 -wt 25 -r {3}/resolvers.txt -o {0} -v -list {1} -d {2}".format(path, domains, domain,self.resources))
return
#enumerates subdomains using github-subdomains
def gitdomain(self):
domain = self.domain
path = self.path
api=self.githubapi
output = path+"/gitdomain.log"
if(os.path.exists(output)):
os.system("mv {0} {0}.old".format(output))
os.system("github-subdomains -d {1} -t {2} > {0}".format(output, domain, api))
return
#enumerates subdomains using subfinder
#"retains wildcard domains"
def subfinder(self):
domain = self.domain
path = self.path
output = path+"/subfinder.log"
if(os.path.exists(output)):
os.system("mv {0} {0}.old".format(output))
os.system("subfinder -all -recursive -t 50 -max-time 20 -o {0} -v -timeout 20 -d {1}".format(output, domain))
return
#enumerates subdomains using shuffledns
#"removes wildcard domains"
def shuffledns(self):
self.getresolvers()
self.generateSubdomainsWordist()
domain = self.domain
path = self.path
output = path+"/shuffledns.log"
if(os.path.exists(output)):
os.system("rm {0}".format(output))
os.system("shuffledns -retries 10 -strict-wildcard -wt 30 -r {2}/resolvers.txt -w {2}/subdomains.txt -o {0} -v -d {1}".format(output, domain, self.resources))
self.shuffsolv(output, domain)
os.system("rm {0} && mv {1} {0}".format(output, path+"/shuffsolv.log"))
return
#probes for web servers using httpx
def httpx(self, domains, output, extras=""):
os.system("httpx {2} -no-color -l {0} -threads 100 -retries 2 -timeout 6 -verbose -o {1}".format(domains, output, extras))
return
#enumerates urls using gau, filters using gf & probes using httpx
def gau(self):
domain = self.domain
path = self.path
path+="/gau.log"
if(os.path.exists(path)):
os.system("mv {0} {0}.old".format(path))
os.system("gau -subs -o {0} {1}".format(path, domain))
out = self.path+"/gauModP.log"
os.system("cat {0} | gf params | sed 's/=[^&]*/=ALTER/g' | sort -u > {1}".format(path, out))
inp = out
out = self.path+"/gttpxP.log"
self.httpx(inp, out)
out = self.path+"/gauModF.log"
os.system("cat {0} | gf files | sort -u > {1}".format(path, out))
inp=out
out = self.path+"/gttpxF.log"
self.httpx(inp, out)
return
#enumerates urls using github-endpoints, filters using gf & probes using httpx
def giturl(self):
domain = self.domain
path = self.path
path+="/giturl.log"
api = self.githubapi
if(os.path.exists(path)):
os.system("mv {0} {0}.old".format(path))
os.system("github-endpoints -a -t {2} -d {1} > {0}".format(path, domain, api))
out = self.path+"/giturlModP.log"
os.system("cat {0} | gf params | sed 's/=[^&]*/=ALTER/g' | sort -u > {1}".format(path, out))
inp=out
out = self.path+"/gittpxP.log"
self.httpx(inp, out)
out = self.path+"/giturlModF.log"
os.system("cat {0} | gf files | sort -u > {1}".format(path, out))
inp=out
out = self.path+"/gittpxF.log"
self.httpx(inp, out)
return
#removes log files & empty files
def remlog(self):
os.system("rm {0}/*.log*".format(self.path))
os.system("find {0} -type f -empty -delete".format(self.path))
| [
"26509147+g147@users.noreply.github.com"
] | 26509147+g147@users.noreply.github.com |
3f1d82ec3f55da9bd1f1de1c699a519c97ca65e6 | 886fbd993dfda4afc402e49ef6910e631f62a1af | /2015/day18/task2.py | 8472e44b6d9708832a5a386c6c95621f51d4bf8e | [] | no_license | EliTheCreator/AdventOfCode | 6fb59eeddfad3592bbd6628850063259ce65899b | ee1eb7e26dba4b23eb3152dcfca9a44aff8ecc67 | refs/heads/master | 2022-05-03T13:53:32.837102 | 2022-03-03T21:10:11 | 2022-03-03T21:10:11 | 227,398,352 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,275 | py | from copy import deepcopy
from itertools import product
def main():
with open("input", "r") as file:
grid = [[False] + [False if c == '.' else True for c in line.strip()] + [False]
for line in file.readlines()]
cur = [[False for _ in range(len(grid[0]))]] + grid + \
[[False for _ in range(len(grid[0]))]]
next = deepcopy(cur)
for (row, col) in product((1, len(cur)-2), repeat=2):
cur[row][col] = True
for _ in range(100):
for row in range(1, len(cur) - 1):
for col in range(1, len(cur[0]) - 1):
neighboursOn = 0
nextState = False
for drow, dcol in product(range(-1, 2), repeat=2):
neighboursOn += cur[row + drow][col + dcol]
if cur[row][col] and 3 <= neighboursOn and neighboursOn <= 4:
nextState = True
elif not cur[row][col] and neighboursOn == 3:
nextState = True
next[row][col] = nextState
temp = cur
cur = next
next = temp
for (row, col) in product((1, len(cur)-2), repeat=2):
cur[row][col] = True
print(sum((sum(row) for row in cur)))
if __name__ == "__main__":
main()
| [
"elischmi@ethz.ch"
] | elischmi@ethz.ch |
1238d06533886e1be8395832af859581b5ad7da8 | 4c8247faa8e2e87e5dfb8f27215d561b35e4200d | /aiger_analysis/safety_game.py | d86ccbc16e898832a8f69787749469871f6ec024 | [
"MIT"
] | permissive | mvcisback/py-AIGAR | d05afb1ebc61e596c800279bb33d6e8ddb7153f0 | f1b4874ca88278ee43067cc8d29b8c0839aa8131 | refs/heads/master | 2022-08-17T13:11:02.053523 | 2019-01-19T19:39:11 | 2019-01-19T19:39:11 | 143,582,931 | 0 | 0 | MIT | 2022-07-15T18:41:00 | 2018-08-05T04:23:51 | Python | UTF-8 | Python | false | false | 3,422 | py | #!/usr/bin/env python3
import sys
import argparse
import itertools
from typing import NamedTuple
import aiger
from aiger import BoolExpr, atom
from aiger_analysis import eliminate, is_equal
def _cutlatches_and_rename(aig):
if len(aig.latches) == 0:
return aig
aig, latch_names = aig.cutlatches(aig.latches)
output_map = {new_name: [old_name]
for old_name, (new_name, _) in latch_names.items()}
out_rename_aig = aiger.tee(output_map)
input_map = {old_name: [new_name]
for old_name, (new_name, _) in latch_names.items()}
in_rename_aig = aiger.tee(input_map)
return in_rename_aig >> aig >> out_rename_aig
class Game(NamedTuple):
aig: aiger.AIG
@property
def system(self):
return [x for x in self.inputs if x.startswith('controllable_')]
@property
def environment(self):
return [x for x in self.inputs if not x.startswith('controllable_')]
@property
def output(self):
assert isinstance(self.aig, aiger.AIG)
assert len(self.aig.outputs) is 0
return list(self.aig.outputs)[0]
@property
def inputs(self):
return self.aig.inputs
def is_realizable(self, use_cegar=False, verbose=False):
assert len(self.aig.outputs) is 1
initial_state = {x: val for (x, val) in self.aig.latch2init}
bad = BoolExpr(aiger.sink(self.aig.latches) | atom(False).aig)
transition_relation = \
_cutlatches_and_rename(self.aig) >> \
aiger.bit_flipper(inputs=self.aig.outputs)
for i in itertools.count(): # to infinity and beyond
print(f'Iteration {i+1}')
tmp = transition_relation >> (~bad).aig # do not go to a bad state
miter1 = BoolExpr(tmp >> aiger.and_gate(tmp.outputs))
miter2 = eliminate(miter1, self.system, verbose=verbose)
next_bad = bad | eliminate(~miter2,
self.environment,
verbose=verbose)
# delete comments to avoid them accumulate
next_bad = BoolExpr(next_bad.aig.evolve(comments=()))
if next_bad(inputs=initial_state):
print('Unrealizable')
return False
print('Fixed point check')
if is_equal(bad, next_bad):
print('Realizable')
return True
bad = next_bad
if __name__ == "__main__":
arg_parser = argparse.ArgumentParser(
description="A safety game solver using repeated projections.")
arg_parser.add_argument('--cegar', dest='cegar', action='store_true',
help="Support CADET' projection with CEGAR.")
arg_parser.add_argument('-v', '--verbose', dest='verbose',
action='store_true',
help="More output; including CADET -v 1.")
arg_parser.add_argument('input_file', action='store', nargs='?',
type=str,
help='Input file in extended AIGER format')
args = arg_parser.parse_args()
file_name = args.input_file
if file_name is None:
arg_parser.print_help(sys.stderr)
quit(1)
res = Game(aiger.load(file_name)).is_realizable(use_cegar=args.cegar,
verbose=args.verbose)
print(res)
| [
"markus.norman.rabe@gmail.com"
] | markus.norman.rabe@gmail.com |
ef38ac205cecc6f4b41e8688ff2b4f5bb2016917 | 31f34fc3b9164fa64aaca8aa0cac27730f8270ec | /producto/migrations/0003_auto_20200522_1653.py | 19de99f9e21a6f2c35f068d6a76e13962412c09a | [] | no_license | OrlandoRibera/GestorDeVentas | 6de6def14c081f95a9a4f5f99238ab5dd0c4d750 | 513d021a0529433949b99b5bfcf390e9f401a74a | refs/heads/master | 2023-08-05T11:57:52.625628 | 2020-10-05T23:28:53 | 2020-10-05T23:28:53 | 267,130,477 | 1 | 0 | null | 2021-09-22T19:06:10 | 2020-05-26T19:13:39 | Python | UTF-8 | Python | false | false | 514 | py | # Generated by Django 3.0.6 on 2020-05-22 20:53
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('producto', '0002_auto_20200520_1655'),
]
operations = [
migrations.RemoveField(
model_name='producto',
name='categoria',
),
migrations.AddField(
model_name='producto',
name='categoria',
field=models.ManyToManyField(to='producto.Categoria'),
),
]
| [
"37455311+OrlandoRibera@users.noreply.github.com"
] | 37455311+OrlandoRibera@users.noreply.github.com |
59325bfbf4356ed7cd05fd6c5ca3bd435f5230cd | cc44da7bde5439248f01a6a1d18c8e36deaa559b | /attic/mash-app-engine/flexisolr/urls.py | eb1a254bf6ddf05d4a24550f9a3c472102bca779 | [
"LicenseRef-scancode-public-domain"
] | permissive | panna/lab | 767175016fbe5169c3b93ac49b058f095bf4cb12 | 0f46c7d29d2fd90b61e4ef7bdc4b7c8a3857de63 | refs/heads/master | 2020-04-03T11:19:33.126924 | 2009-11-16T20:51:57 | 2009-11-16T20:51:57 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 399 | py | # -*- coding: utf-8 -*-
from django.conf.urls.defaults import *
urlpatterns = patterns('flexisolr.views',
(r'^options.js$', 'options'),
(r'^data.json$', 'data'),
(r'^render.js$', 'render'),
)
urlpatterns += patterns('',
('^yui-demo/$','django.views.generic.simple.direct_to_template',
{'template': 'flexisolr/yui-demo.html'})
)
| [
"admin@crowdsense.com"
] | admin@crowdsense.com |
dae3e480c4b4157b261654cb451fac71d7b69224 | 484b2de4e9690badec6dbff2d01535fa7ee59aa5 | /dbscan_by_hand.py | 702ed98e153549f848df282edb87fe1e03731e4e | [] | no_license | cmdjzs/C_O_D | 0dc0929b6c570bb46c4ce011971ec639811fd486 | ccb420fc119df82bfcb2f18d2cb679a406bda903 | refs/heads/master | 2020-04-24T00:55:17.216322 | 2018-11-20T08:05:43 | 2018-11-20T08:05:43 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,373 | py | # !/usr/bin/env python3
# -*- coding:utf-8 -*-
import math
import numpy as np
import pylab as pl
#数据集：每三个是一组分别是西瓜的编号，密度，含糖量
data = """
1,0.697,0.46,2,0.774,0.376,3,0.634,0.264,4,0.608,0.318,5,0.556,0.215,
6,0.403,0.237,7,0.481,0.149,8,0.437,0.211,9,0.666,0.091,10,0.243,0.267,
11,0.245,0.057,12,0.343,0.099,13,0.639,0.161,14,0.657,0.198,15,0.36,0.37,
16,0.593,0.042,17,0.719,0.103,18,0.359,0.188,19,0.339,0.241,20,0.282,0.257,
21,0.748,0.232,22,0.714,0.346,23,0.483,0.312,24,0.478,0.437,25,0.525,0.369,
26,0.751,0.489,27,0.532,0.472,28,0.473,0.376,29,0.725,0.445,30,0.446,0.459"""
# 数据处理 dataset是30个样本（密度，含糖量）的列表
a = data.split(',')
dataset = [(float(a[i]), float(a[i+1])) for i in range(1, len(a)-1, 3)]
# 计算欧几里得距离,a,b分别为两个元组
def dist(a, b):
return math.sqrt(math.pow(a[0]-b[0], 2)+math.pow(a[1]-b[1], 2))
# 算法模型
def DBSCAN(D, e, Minpts):
# 初始化核心对象集合T,聚类个数k,聚类集合C, 未访问集合P,
T = set()
k = 0
C = []
P = set(D)
for d in D:
if len([i for i in D if dist(d, i) <= e]) >= Minpts:
T.add(d)
# print("T:", T, "\n")
#开始聚类
while len(T):
P_old = P
o = list(T)[np.random.randint(0, len(T))]
P = P - set(o)
Q = []
Q.append(o)
# print("Q:", Q, "\n")
while len(Q):
q = Q[0]
Nq = [i for i in D if dist(q, i) <= e]
# print("Nq:", Nq, "\n")
if len(Nq) >= Minpts:
S = P & set(Nq)
Q += (list(S))
# print("Q+=:", Q, "\n")
P = P - S
Q.remove(q)
k += 1
Ck = list(P_old - P) # 已分类出的簇中的点
# print("Ck:", Ck, "\n")
T = T - set(Ck)
C.append(Ck)
return C
#画图
def draw(C):
colValue = ['r', 'y', 'g', 'b', 'c', 'k', 'm']
for i in range(len(C)):
coo_X = [] #x坐标列表
coo_Y = [] #y坐标列表
for j in range(len(C[i])):
coo_X.append(C[i][j][0])
coo_Y.append(C[i][j][1])
pl.scatter(coo_X, coo_Y, marker='x', color=colValue[i%len(colValue)], label=i)
pl.legend(loc='upper right')
pl.show()
C = DBSCAN(dataset, 0.11, 5)
draw(C)
| [
"jyz4872@163.com"
] | jyz4872@163.com |
277892508b145e197f2e8e451e059b45ae7d1432 | 0b64e696083d567ed18e6366d8bd8e99733e1485 | /node_modules/socket.io/node_modules/redis/node_modules/hiredis/build/c4che/Release.cache.py | 760ac556fb84b9aa1a219e95c3e78caf69dbbc15 | [
"MIT"
] | permissive | iambibhas/myn3 | 340286d56edcde4ad024b63f0b12e1ecb7c6b15f | 994c2850ac76920289004dc67f46bcedf7e652dc | refs/heads/master | 2021-01-01T05:53:35.376823 | 2012-09-24T20:20:16 | 2012-09-24T20:20:16 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,449 | py | AR = '/usr/bin/ar'
ARFLAGS = 'rcs'
CCFLAGS = ['-g']
CCFLAGS_MACBUNDLE = ['-fPIC']
CCFLAGS_NODE = ['-D_LARGEFILE_SOURCE', '-D_FILE_OFFSET_BITS=64']
CC_VERSION = ('4', '6', '3')
COMPILER_CXX = 'g++'
CPP = '/usr/bin/cpp'
CPPFLAGS_NODE = ['-D_GNU_SOURCE']
CPPPATH_NODE = '/usr/include/nodejs'
CPPPATH_ST = '-I%s'
CXX = ['/usr/bin/g++']
CXXDEFINES_ST = '-D%s'
CXXFLAGS = ['-g', '-Wall', '-O3']
CXXFLAGS_DEBUG = ['-g']
CXXFLAGS_NODE = ['-D_LARGEFILE_SOURCE', '-D_FILE_OFFSET_BITS=64']
CXXFLAGS_RELEASE = ['-O2']
CXXLNK_SRC_F = ''
CXXLNK_TGT_F = ['-o', '']
CXX_NAME = 'gcc'
CXX_SRC_F = ''
CXX_TGT_F = ['-c', '-o', '']
DEST_BINFMT = 'elf'
DEST_CPU = 'x86_64'
DEST_OS = 'linux'
FULLSTATIC_MARKER = '-static'
LIBDIR = '/home/bibhas/.node_libraries'
LIBPATH_HIREDIS = '../deps/hiredis'
LIBPATH_NODE = '/usr/lib'
LIBPATH_ST = '-L%s'
LIB_HIREDIS = 'hiredis'
LIB_ST = '-l%s'
LINKFLAGS_MACBUNDLE = ['-bundle', '-undefined', 'dynamic_lookup']
LINK_CXX = ['/usr/bin/g++']
NODE_PATH = '/home/bibhas/.node_libraries'
PREFIX = '/usr/local'
PREFIX_NODE = '/usr'
RANLIB = '/usr/bin/ranlib'
RPATH_ST = '-Wl,-rpath,%s'
SHLIB_MARKER = '-Wl,-Bdynamic'
SONAME_ST = '-Wl,-h,%s'
STATICLIBPATH_ST = '-L%s'
STATICLIB_MARKER = '-Wl,-Bstatic'
STATICLIB_ST = '-l%s'
macbundle_PATTERN = '%s.bundle'
program_PATTERN = '%s'
shlib_CXXFLAGS = ['-fPIC', '-DPIC']
shlib_LINKFLAGS = ['-shared']
shlib_PATTERN = 'lib%s.so'
staticlib_LINKFLAGS = ['-Wl,-Bstatic']
staticlib_PATTERN = 'lib%s.a'
| [
"iambibhas@gmail.com"
] | iambibhas@gmail.com |
0ad37103fdecdba74b5c20b07a0ac4f94921f8c8 | 53c7ee287f797987d53221080d87df96734d143d | /bartending/models.py | 97ded108232a181b41740c2a08814bc74d572d75 | [] | no_license | QuinnMcHugh/Mixology | e05736c6d58fd7945a78b51e10e4d296a7e2d7d3 | 74eadc166651ab138efc32e77d6e61a2e30fe3c2 | refs/heads/master | 2020-03-19T00:54:07.106922 | 2018-05-31T17:05:15 | 2018-05-31T17:05:15 | 135,508,819 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,037 | py | # This is an auto-generated Django model module.
# You'll have to do the following manually to clean this up:
# * Rearrange models' order
# * Make sure each model has one field with primary_key=True
# * Make sure each ForeignKey has `on_delete` set to the desired behavior.
# * Remove `managed = False` lines if you wish to allow Django to create, modify, and delete the table
# Feel free to rename the models, but don't rename db_table values or field names.
from django.db import models
from django.conf import settings
class Direction(models.Model):
id = models.IntegerField(primary_key=True)
instruction = models.TextField(blank=True, null=True)
class Meta:
managed = False
db_table = 'direction'
class Drink(models.Model):
id = models.IntegerField(primary_key=True)
name = models.TextField(blank=True, null=True)
class Meta:
managed = False
db_table = 'drink'
class Ingredient(models.Model):
id = models.IntegerField(primary_key=True)
name = models.TextField(blank=True, null=True)
class Meta:
managed = False
db_table = 'ingredient'
class Serving(models.Model):
id = models.IntegerField(primary_key=True)
measurement = models.TextField(blank=True, null=True)
class Meta:
managed = False
db_table = 'serving'
class Recipe(models.Model):
id = models.IntegerField(primary_key=True)
steporder = models.IntegerField()
drink = models.IntegerField()
ingredient = models.IntegerField()
serving = models.IntegerField()
direction = models.IntegerField()
class Meta:
managed = False
db_table = 'recipe'
class Favorite(models.Model):
drink = models.ForeignKey(Drink, on_delete=models.CASCADE)
user = models.ForeignKey(settings.AUTH_USER_MODEL, on_delete=models.CASCADE)
class MyBar(models.Model):
user = models.ForeignKey(settings.AUTH_USER_MODEL, on_delete=models.CASCADE)
ingredient = models.ForeignKey(Ingredient, on_delete=models.CASCADE)
| [
"quinnmchugh.us@gmail.com"
] | quinnmchugh.us@gmail.com |
c91d07ac70bccd377e5acca2c18ed2bf40df3aa9 | 5830c77c25f1bbc1421b8a976cafa05f6daefba5 | /lib/layers/modules/ffm_v3.py | 0cc2318290a98119bfe3887f76970a67c633730c | [] | no_license | BongkyuHwang/m2det | 9d8a99b35ad14b2feaeb073c72d5d040ba467ac0 | 8dc8d85ee9fe6e5d4e624941395dcd373b7022a1 | refs/heads/master | 2020-04-23T15:26:59.792453 | 2019-03-20T05:50:31 | 2019-03-20T05:50:31 | 171,265,639 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 850 | py | import torch
from .base_block import conv_block
class FFMv3(torch.nn.Module):
def __init__(self, in_channels=[4320, 2160, 1080], out_channels=[540, 270, 135]):
super(FFMv3, self).__init__()
self.block1 = conv_block(in_channels[0], out_channels[0], 1, 1)
self.block2 = conv_block(in_channels[1], out_channels[1], 3, 1, 1)
self.block3 = conv_block(in_channels[2], out_channels[2], 3, 1, 1)
def forward(self, deep, mid, shallow):
return torch.cat([
torch.nn.functional.interpolate(
self.block1(deep), scale_factor=4, mode="bilinear", align_corners=True
),
torch.nn.functional.interpolate(
self.block2(mid), scale_factor=2, mode="bilinear", align_corners=True
),
self.block3(shallow)
], dim=1
)
| [
"mcmasruntotop@gmail.com"
] | mcmasruntotop@gmail.com |
c66230f4d5c3a73b2c1abeda25bbdde19078a08c | fa97330cd674ead06615e284f8daaa4df3844aa7 | /fact/challenge5.py | 3399e8f2702840759930b1d877549d3fc62f5dd4 | [] | no_license | ashenoy2004/mycode | 36483f24865b9cea251505d3169c1540c6a6728c | c469c724b6c3be8bf24cb0d1f0ecd88a39292a7f | refs/heads/main | 2023-04-16T21:28:43.370731 | 2021-04-28T18:12:54 | 2021-04-28T18:12:54 | 361,796,551 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 780 | py | #!/usr/bin/env python3
farms = [{"name": "NE Farm", "agriculture": ["sheep", "cows", "pigs", "chickens", "llamas", "cats"]},
{"name": "W Farm", "agriculture": ["pigs", "chickens", "llamas"]},
{"name": "SE Farm", "agriculture": ["chickens", "carrots", "celery"]}]
choose_farm = input('pick a farm [NE Farm, W Farm, SE Farm] ')
farmKey=""
for data in farms:
for key,value in data.items():
#print (key, "->", value)
if (key=="name"):
farmKey =value
elif farmKey==choose_farm and key=="agriculture":
#print(value)
for agridata in value:
#print(agridata,"test.....")
if(agridata not in ('carrots','celery')):
print( agridata)
| [
"ashenoy2004@hotmail.com"
] | ashenoy2004@hotmail.com |
d1c8579dd90a556216015b8d74a011c09a1618b6 | c53a3fa153d92dc700bc3203d5170391e1e2e848 | /interviewProblems/reorderLogs.py | 0a88a96eecfa0adf1680cf7fa6e1935b0c25e75a | [] | no_license | robahall/algosds | 326963e605b2e45a6b67d4abf036d57b8fcc92cb | 8482bd12369b1f18faa4ac19bc3423750fea4695 | refs/heads/master | 2023-02-02T18:00:47.793884 | 2020-12-17T04:24:15 | 2020-12-17T04:24:15 | 290,203,416 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 329 | py |
def reorderLogFiles(logs):
def f(log):
id_, rest = log.split(" ", 1)
return (0, rest, id_) if rest[0].isalpha() else (1,)
return sorted(logs, key = f)
if __name__ == "__main__":
test = ["dig1 8 1 5 1", "let1 art can", "dig2 3 6", "let2 own kit dig", "let3 art zero"]
print(reorderLogFiles(test)) | [
"robahall2@gmail.com"
] | robahall2@gmail.com |
f02e021b93ad7e16e0802a4001543c39c28f75eb | 5c1dd485a0b079dc24ffdc09cf6e14beb58a28b8 | /experiments/germ-poa/plot.py | c592e9859011dd91d1758702c012c0c555629125 | [] | no_license | biotungsten/Jufo2021 | f924ab6356db3846759aeaf78653f3b577081aba | 47be4fc1aa44c1660d29a6b7ec26c2d4b84b468a | refs/heads/master | 2023-05-06T04:23:00.866355 | 2021-05-28T07:45:47 | 2021-05-28T07:45:47 | 320,343,909 | 7 | 0 | null | 2021-03-28T18:34:20 | 2020-12-10T17:33:19 | Jupyter Notebook | UTF-8 | Python | false | false | 1,792 | py | import csv
from scipy.stats import ttest_ind_from_stats
import matplotlib.pyplot as plt
from utils.bh import benjaminiHochberg
#run this script from experiments dir with -m option
#results are in analysis.txt
#import data
data = dict()
with open("germ-poa/aggregated.csv", 'r') as csvfile:
reader = csv.reader(csvfile)
for idx, row in enumerate(reader):
if idx==0:
continue
data[row[0]+row[1]] = [float(row[2]) , float(row[3])]
#extract ctrl
ctrl = data["CTRLm"]
#statistics
p = []
pbonf = 0.05/(len(data.keys())-4) #-4 because no test is effectively performed for malonic acid and CTRL
for key,val in data.items():
if key in ["MAh", "MAm", "MAl", "CTRLm"]:
continue
stat = ttest_ind_from_stats(val[0], val[1], 4, ctrl[0], ctrl[1], 4, equal_var=False)
p.append(stat.pvalue)
print("{} \t\t has p={} and t={} \t significance:{}".format(key, round(stat.pvalue, 3), round(stat.statistic, 3), stat.pvalue<pbonf))
print("\nBonferroni corrected alpha is {}".format(round(pbonf, 3)))
#do bonferroni holm correction
holm = benjaminiHochberg(p, 0.05)
print("-----------")
print("Bonferroni-Holm significances (in same order as results)")
print(*holm)
#do plotting
fig = plt.figure()
ax = plt.subplot(111)
ax.bar([n for n in range(len(data.values()))],[n[0] for n in data.values()], color="orange", yerr=[n[1] for n in data.values()], edgecolor = 'black', capsize=7, label='1 Tag', error_kw=dict(capsize=2, elinewidth=0.5))
ax.set_xticks([r for r in range(len(data.values()))])
ax.set_xticklabels(data.keys(), rotation=45)
ax.set_ylabel("Keimungsrate [%]")
ax.set_yticklabels([0,20,40,60,80,100])
ax.set_title("Keimungsrate von P. annua nach 7 Tagen")
ax.set_ylim([0,1])
fig.savefig("germ-poa/germpoa.png", dpi=600, bbox_inches='tight')
| [
"sauerdavid13@icloud.com"
] | sauerdavid13@icloud.com |
d7a3fc279945d718264b09b2c104593f7188efe5 | bf1cf014b2965fb8d38f7a58cc812dcd65719ed9 | /interviewProblems/houses.py | dff6baad32a3dcda17fb948d24a387765e8e5731 | [] | no_license | imthefrizzlefry/PythonPractice | c07408592b0f5234217ec448cd2b81415332afbc | 954a25e77435d97a1a0f12c38568ee68686e6960 | refs/heads/master | 2021-02-20T16:23:56.483052 | 2020-06-13T08:08:54 | 2020-06-13T08:08:54 | 245,341,475 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,671 | py | ''' Demo question from Amazon Demo Assessment
Inspired by the game of life, but in an array rather than a n-dimentional grid.
each element in the array is a "house" competing with it's neighbors. Each house has a binary state 1=active 0=inactive.
When both neighbors are either active or inactive, the current house will become inactive.
The frist and last element in the array have an inactive neighbor outisde of the array.
If a house has one active and one inactive neighbor, then it will be active on the next day.
Given these rules, create a method that will calculate the state after X number of days.
Example:
cellCompete([1],1) -> [0] # this is because the imaginary neighbors to the left and right are both inactive.
cellCompete([1,1], 100) -> [1,1] # this is because the imaginary neighbors are both inactive, but the real neighbor is active. So they never change.
cellCompete([1,0,1,1],6) -> [0,0,1,1] -> [0,1,1,1] -> [1,1,0,1] -> [1,1,0,0] -> [1,1,1,0] -> [1,0,1,1] # I iterated through the 6 changes here
'''
def cellCompete(states, days):
# WRITE YOUR CODE HERE
if days == 0 or states is None: return states
ret = [0]*len(states)
for _ in range(days):
for i in range(len(states)):
if i == 0:
ret[i] = 0 if len(states) == 1 or states[i+1] == 0 else 1
elif i == len(states)-1:
ret[i] = 0 if states[i-1] == 0 else 1
else:
ret[i] = 0 if states[i-1] == states[i+1] else 1
states = ret[:]
return ret
print(cellCompete([1,1,1,0,1,1,1,1],2))
print(cellCompete([1],2))
print(cellCompete([1],0))
print(cellCompete([],2)) | [
"imthefrizzlefry@gmail.com"
] | imthefrizzlefry@gmail.com |
8beb1fbfd116c9005114d45d4c3a18b38e31e0a7 | f731ea96b45fa42954d2c81b43992484ecc55f2e | /exam_project/api_v1/serializers.py | bf77008975e2184739447d3562f54a3f15da4efb | [] | no_license | ArmaniEt/tasks-api | f170b820f3e85a4227e2791906876a32fd59c89a | 7760675941fc946956cb2d5cf148ef0e88a20fe1 | refs/heads/master | 2020-04-27T21:31:26.540567 | 2019-03-09T14:08:13 | 2019-03-09T14:08:13 | 174,700,660 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 249 | py | from rest_framework import serializers
from webapp.models import Task
class TaskSerializer(serializers.ModelSerializer):
class Meta:
model = Task
fields = ('id', 'summary', 'description', 'due_date', 'status', 'time_planned')
| [
"razzarioa@gmail.com"
] | razzarioa@gmail.com |
1b0893cba4fe2e7fba672064506ea54c2252585a | dd7a0de707e995851bcb278d04e18f8402429338 | /d4/main1.py | 225647ed3a55e034b574748289cc9770d008b93f | [] | no_license | aexhg/aof2 | f91ce9cb3ab8668ea0ae8fb04ec0eb30e2435867 | 0f89e60717cc73d889718b5215ff80665b639fd7 | refs/heads/master | 2023-02-25T01:18:47.301723 | 2021-01-31T15:08:51 | 2021-01-31T15:08:51 | 334,687,001 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,192 | py | # byr (Birth Year)
# iyr (Issue Year)
# eyr (Expiration Year)
# hgt (Height)
# hcl (Hair Color)
# ecl (Eye Color)
# pid (Passport ID)
# cid (Country ID)
required_keys = {'byr', 'iyr', 'eyr', 'hgt', 'hcl', 'ecl', 'pid', 'cid'}
optional_keys = {'cid'}
filename = "./d4/input.txt"
passports = []
with open(filename) as f:
passports = []
passport = {}
for line in f.readlines():
if len(line.strip()) == 0:
passports.append(passport)
passport = {}
else:
fields = line.strip().split(' ')
for field in fields:
f, v = field.split(':')
passport.update({f:v})
if passports:
passports.append(passport)
# byr (Birth Year) - four digits; at least 1920 and at most 2002.
# iyr (Issue Year) - four digits; at least 2010 and at most 2020.
# eyr (Expiration Year) - four digits; at least 2020 and at most 2030.
# hgt (Height) - a number followed by either cm or in:
# If cm, the number must be at least 150 and at most 193.
# If in, the number must be at least 59 and at most 76.
# hcl (Hair Color) - a # followed by exactly six characters 0-9 or a-f.
# ecl (Eye Color) - exactly one of: amb blu brn gry grn hzl oth.
# pid (Passport ID) - a nine-digit number, including leading zeroes.
# cid (Country ID) - ignored, missing or not.
def validate_passport(passport):
def _check_valid_int(value, count, lb, ub):
if len(value) != count:
return False
ivalue = int(value)
if ivalue < lb or ivalue > ub:
return False
return True
byr = passport['byr']
if not _check_valid_int(byr, 4, 1920, 2002):
return False
iyr = passport['iyr']
if not _check_valid_int(iyr, 4, 2010, 2020):
return False
eyr = passport['eyr']
if not _check_valid_int(eyr, 4, 2020, 2030):
return False
def count_valid(passports, required_keys, optional_keys):
count = 0
for p in passports:
s = set(p.keys())
if len(required_keys - optional_keys - s) == 0:
count += 1
return count
print(f'valid count: {count_valid(passports, required_keys, optional_keys)}')
| [
"aexhg@gmail.com"
] | aexhg@gmail.com |
47c4dfc1e15fb0f15d11f9e64213a4ad1ec7b299 | f4f19a0b856ba36100f67272b05dad90c76b7457 | /pre_processing/pre_process.py | d798aafcbd20c54aaac01bdeef281b7092d8d104 | [] | no_license | JamesBrace/kaggle | b9d8130aa1b5d17a2d89f3fa64b1142eb7167f7e | 2069a5a3afa236bf57b25526439b5d5950e4b136 | refs/heads/master | 2021-04-26T22:28:18.676810 | 2018-03-06T18:25:43 | 2018-03-06T18:25:43 | 124,097,839 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,993 | py | import numpy as np
from skimage.feature import canny
from scipy import ndimage as ndi
import matplotlib.pyplot as plt
from skimage import morphology
from skimage import util
class PreProcessedData:
def __init__(self):
self.x = []
self.y = []
self.cannied_images = []
self.filled_images = []
self.cleaned_images = []
self.processed_images = []
print("Getting and reshaping data")
self.get_and_reshape_data()
print("Pre-processing data")
self.pre_process()
def get_and_reshape_data(self):
self.x = np.loadtxt("../data/train_x.csv", delimiter=",") # load from text
self.y = np.loadtxt("../data/train_y.csv", delimiter=",")
self.x = self.x.reshape(-1, 64, 64) # reshape
self.y = self.y.reshape(-1, 1)
plt.imshow(self.x[0], cmap='gray')
plt.show()
def pre_process(self):
print("Canny-ing images")
self.canny_images()
print("Filling images")
self.fill_images()
print("Cleaning images")
self.clean_images()
def canny_images(self):
print(self.x[0])
print("Inverting images")
inverted_images = list(map(util.invert, self.x))
plt.imshow(inverted_images[0], cmap='gray')
plt.show()
print("Cannying inverted images")
self.cannied_images = list(map(canny, inverted_images))
# Done for test purposes
self.display_canny_image_example()
def display_canny_image_example(self):
plt.imshow(self.cannied_images[0], cmap='gray')
plt.show()
fig, ax = plt.subplots(figsize=(64, 64))
ax.imshow(self.cannied_images[0], cmap=plt.cm.gray, interpolation='nearest')
ax.set_title('Canny detector')
ax.axis('off')
ax.set_adjustable('box-forced')
plt.show()
def fill_images(self):
self.filled_images = list(map(ndi.binary_fill_holes, self.cannied_images))
self.cannied_images = []
# Done for test purposes
self.display_filled_image_example()
def display_filled_image_example(self):
plt.imshow(self.filled_images[0], cmap='gray')
plt.show()
fig, ax = plt.subplots(figsize=(64, 64))
ax.imshow(self.filled_images[0], cmap=plt.cm.gray, interpolation='nearest')
ax.set_title('filling the holes')
ax.axis('off')
plt.show()
def clean_images(self):
self.cleaned_images = morphology.remove_small_objects(self.filled_images, 21)
self.filled_images = []
self.display_clean_image_example()
def display_clean_image_example(self):
plt.imshow(self.cleaned_images[0], cmap='gray')
plt.show()
fig, ax = plt.subplots(figsize=(64, 64))
ax.imshow(self.cleaned_images[0], cmap=plt.cm.gray, interpolation='nearest')
ax.set_title('filling the holes')
ax.axis('off')
plt.show()
data = PreProcessedData()
| [
"james.brace@mail.mcgill.ca"
] | james.brace@mail.mcgill.ca |
8fb32942c0334dafeca7170fa777dd8a65c4d1c0 | daf87d82fc4150f2ffedd5863b41cdbd1dc470af | /Frootwala_Project/Offers/migrations/0001_initial.py | 1144a4ad60f8cc6195dbceb6e6cef86185ccaa69 | [] | no_license | punitda/FrootCart_Project | 3d76a7e4516963c2812d47c4ba9c3a278693bdae | 772b470c8aae49b7214a474c883a35c827e42182 | refs/heads/master | 2021-05-29T21:06:28.875635 | 2015-10-24T13:31:06 | 2015-10-24T13:31:06 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 466 | py | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
class Migration(migrations.Migration):
dependencies = [
]
operations = [
migrations.CreateModel(
name='Offers',
fields=[
('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),
('url', models.URLField()),
],
),
]
| [
"punitdama@gmail.com"
] | punitdama@gmail.com |
a269a1e85ba396ffd695782080995716f9d067df | b22831a11ea76e808c2dade8a7c90e83976b81f5 | /alembic/versions/021_pago.py | 39645939190b9b7b6b25e02881e91bfa3a0c1911 | [
"MIT"
] | permissive | tzulberti/entrenamiento-arqueria | d9eb3a7379bd1b02fddcaa56f2a3c8f85af067a4 | d02cb055393bce405e9b3892c9f91ae163b6db06 | refs/heads/master | 2021-03-12T22:47:50.192563 | 2015-02-26T00:13:09 | 2015-02-26T00:13:09 | 14,096,242 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 910 | py | """pago
Revision ID: 020
Revises: 019
Create Date: 2014-05-28 07:16:44.057774
"""
# revision identifiers, used by Alembic.
revision = '021'
down_revision = '020'
from alembic import op
import sqlalchemy as sa
def upgrade():
op.create_table('pago',
sa.Column('id', sa.Integer, primary_key=True),
sa.Column('id_razon_pago', sa.Integer, sa.ForeignKey('razon_pago.id'), nullable=False),
sa.Column('id_arquero', sa.Integer, sa.ForeignKey('arquero.id'), nullable=False),
sa.Column('id_cargado_por', sa.Integer, sa.ForeignKey('usuario.id'), nullable=False),
sa.Column('cuando', sa.Date, nullable=False),
sa.Column('mes_correspondiente', sa.Date, nullable=False),
sa.Column('importe', sa.Float, nullable=False),
sa.Column('comprobante_path', sa.Text),
sa.Column('comentario', sa.Text)
)
def downgrade():
op.drop_table('pago')
| [
"tzulberti@gmail.com"
] | tzulberti@gmail.com |
12c2ebd2805054b5a8256563e9d630b85b51c03c | 3d2d9534a5e51e4f9601df5110bda3910f6b1613 | /main.py | dc3318298ff365b5de7f8c8d2fccd480255e74ab | [] | no_license | AliceB08/closest_points | 050bd17993490ec45aa4ac15eb8438c291c22fbc | 9323df67d7397b8a7f7070af4b21aea1cd505f81 | refs/heads/master | 2020-07-23T13:21:58.310747 | 2019-09-10T14:00:59 | 2019-09-10T14:02:28 | 207,570,918 | 0 | 2 | null | null | null | null | UTF-8 | Python | false | false | 1,651 | py | import random
import math
import sys
import time
import csv
from brute_force import execute_brute_force
from DpR import execute_DpR
from utils import GRID_SIZE
ALGO = sys.argv[1] # Algo à utiliser DPR ou BF
NB_POINTS = int(sys.argv[2]) # Nombre de points à générer
'''
Un point est représenté par un tuple (position_x, position_y)
La fonction generate_points génère une liste de N points.
'''
def generate_points(N):
points = [(random.randint(0, GRID_SIZE), random.randint(0, GRID_SIZE)) for i in range(N)]
return points
'''
--------------------------------------------------------------------
ATTENTION : Dans votre code vous devez utiliser le générateur gen.py
pour générer des points. Vous devez donc modifier ce code pour importer
les points depuis les fichiers générés.
De plus, vous devez faire en sorte que l'interface du tp.sh soit
compatible avec ce code (par exemple l'utilisation de flag -e, -a, (p et -t)).
--------------------------------------------------------------------
'''
def main(algo, nb_points):
POINTS = generate_points(nb_points)
sorted_points_x = sorted(POINTS, key=lambda x: x[0])
sorted_points_y = sorted(POINTS, key=lambda x: x[1])
if algo == "BF":
# Exécuter l'algorithme force brute
time_BF = execute_brute_force(sorted_points_x)
print("Temps : ", time_BF)
elif algo == "DPR":
# Exécuter l'algorithme Diviser pour régner
SEUIL_DPR = 3
time_DPR = execute_DpR(sorted_points_x, sorted_points_y, SEUIL_DPR)
print("Temps : ", time_DPR)
main(ALGO, NB_POINTS) | [
"breton.alice.08@gmail.com"
] | breton.alice.08@gmail.com |
faa44ef39573389312ba3691e39e4ef4c35647ac | cbf128188137e0ac332da25be6ea5f582e973c88 | /python/mytestre.py | 750b22a86380d2dd31c62e0988e3fac9d23f38db | [] | no_license | zhuanshujianghai/Python | dfb0ca980e8fb06bcfa7f86be7b70522a64d05fb | 8238f0c37716869a14a8b79a318abe32a8891670 | refs/heads/master | 2021-05-07T22:01:22.745882 | 2017-12-09T07:54:17 | 2017-12-09T07:54:17 | 109,078,196 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 8,045 | py | import urllib.request
import re
import urllib.parse
import urllib.error
import urllib
import os
import socket
import http.client
import telnetlib
http.client.HTTPConnection._http_vsn = 10
http.client.HTTPConnection._http_vsn_str = 'HTTP/1.0'
#检查代理ip是否可用
def checkip(ip,port):
try:
tn = telnetlib.Telnet(ip, port=port, timeout=1)
except:
print("这个代理IP(" + ip + ":" + port + ")竟然没用")
return False
else:
return True
path = "D:/git_repertory/Python/python"
listip = ""
def pachong(url,ge,ceng,filder,addurl,charset):
global listip
try:
if url.index("//", 0, 2) == 0:
url = url[2:]
except Exception as err:
pass
if url.find("login") >= 0:
return []
if "www" in url or "http" in url or "https" in url:
b = b'/:?=&'
link = urllib.parse.quote(url, b)
url = link
print(url)
else:
url = addurl + url
url = url.replace(".com//", ".com/")
b = b'/:?=&'
url = urllib.parse.quote(url, b)
print(url)
headers = ("User-Agent",
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/61.0.3163.100 Safari/537.36")
opener = urllib.request.build_opener()
opener.addheaders = [headers]
try:
data = opener.open(url).read().decode(charset, "ignore")
patip = "<td>(\d+?\.\d+?\.\d+?.\d+?)</td>"
patport = "<td>(\d+?)</td>"
allip = re.compile(patip).findall(data)
allport = re.compile(patport).findall(data)
if len(allip)==len(allport):
for i in range(len(allip)):
if checkip(allip[i],allport[i]):
content = allip[i]+":" + allport[i]+"\n"
if content not in listip:
listip = listip + content
print(allip[i]+":"+ allport[i]+"***************************")
except urllib.request.URLError as err:
print("*************************************" + url)
return []
except socket.timeout as err:
print("*************************************" + url)
return []
except http.client.IncompleteRead as err:
print("*************************************" + url)
return []
except Exception as err:
print("*************************************" + url)
return []
pat = "<a target=\"_blank\" href=\"(.*?)\""
pat1 = "<a href=\"(.*?)\""
pat2 = "<a class=\"false\" href=\"(.*?)\""
alllink = re.compile(pat).findall(data)
alllink1 = re.compile(pat1).findall(data)
alllink2 = re.compile(pat2).findall(data)
alllink.extend(alllink1)
alllink.extend(alllink2)
new_alllink = []
for link in alllink:
#if link not in new_alllink and ("html" in link or "http:" in link):
if link not in new_alllink:
new_alllink.append(link)
i = 0
thisurl=""
for link in new_alllink:
try:
if link.index("//",0,2)==0:
link = link[2:]
except Exception as err:
pass
if link.find("login")>=0:
continue
if "www" in link or "http" in link or "https" in link:
b = b'/:?=&'
link = urllib.parse.quote(link, b)
thisurl=link
else:
url = addurl + link
url = url.replace(".com//", ".com/")
b = b'/:?=&'
url = urllib.parse.quote(url, b)
thisurl=url
try:
data = opener.open(url).read().decode(charset, "ignore")
patip = "<td>(\d*?\.\d*?\.\d*?.\d*?)</td>"
patport = "<td>(\d*?)</td>"
allip = re.compile(patip).findall(data)
allport = re.compile(patport).findall(data)
if len(allip) == len(allport):
for i in range(len(allip)):
if checkip(allip[i], allport[i]):
content = allip[i] + ":" + allport[i] + "\n"
if content not in listip:
listip = listip + content
print(allip[i] + ":" + allport[i] + "***************************")
except urllib.request.URLError as err:
print("*************************************"+thisurl)
fh = open("D:\git_Repertory\Python\python\\"+filder+"\\"+filder+"_error.txt","a+")
fh.write(thisurl+"\nURLError\n")
fh.close()
# 判断是否存在状态码
if hasattr(err, "code"):
print(err.code)
# 判断是否存在原因
if hasattr(err, "reason"):
print(err.reason)
except socket.timeout as err:
print("*************************************" + thisurl)
fh = open("D:\git_Repertory\Python\python\\" + filder + "\\" + filder + "_error.txt", "a+")
fh.write(thisurl + "\ntimeout\n")
fh.close()
except http.client.IncompleteRead as e:
print("*************************************" + thisurl)
fh = open("D:\git_Repertory\Python\python\\" + filder + "\\" + filder + "_error.txt", "a+")
fh.write(thisurl + "\nIncompleteRead\n")
fh.close()
except Exception as err:
print("*************************************" + thisurl)
fh = open("D:\git_Repertory\Python\python\\" + filder + "\\" + filder + "_error.txt", "a+")
fh.write(thisurl + "\nException\n")
fh.close()
i = i + 1
fh = open("D:\git_Repertory\PythonFile\\20171111ip.txt", "w")
fh.write(listip)
fh.close()
return new_alllink
def digui(alllink,ge,ceng,filder,addurl,charset):
thisalllink=[]
for link in alllink:
templink = pachong(link,ge,ceng,filder,addurl,charset)
if templink!=[]:
thisalllink.extend(templink)
ge = ge + 1
if(len(thisalllink)>0):
ceng = ceng +1
ge=1
print("开始爬取第" + str(ceng) + "层")
digui(thisalllink,ge,ceng,filder,addurl,charset)
else:
print("爬取完毕")
# url = ["http://www.135store.com"]
# filder = "135store"
# addurl = "http://www.135store.com/"
#charset = "utf-8"
# url = ["http://bbs.fuling.com/"]
# filder = "fufeng"
# addurl = "http://bbs.fuling.com/"
# charset = "gbk"
# url = ["http://www.taobao.com/"]
# filder = "taobao"
# addurl = "http://www.taobao.com/"
# charset = "utf-8"
url = ["http://www.xicidaili.com"]
filder = "xicidaili"
addurl = "http://www.xicidaili.com/"
charset = "UTF-8"
digui(url,1,1,filder,addurl,charset)
# url = "http://www.135store.com"
# file = urllib.request.urlopen(url)
# print(file.info())
# headers = ("User-Agent","Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/61.0.3163.100 Safari/537.36")
# opener = urllib.request.build_opener()
# opener.addheaders = [headers]
# data = opener.open(url).read().decode("utf-8","ignore")
# pat = "<a target=\"_blank\" href=\"(.*?)\""
# pat1 = "<a href=\"(.*?)\""
# alllink = re.compile(pat).findall(data)
# alllink1 = re.compile(pat1).findall(data)
# alllink.extend(alllink1)
# new_alllink = []
# for link in alllink:
# if link not in new_alllink and "html" in link:
# new_alllink.append(link)
# i=0
#
# for link in new_alllink:
# if "www" in link or "http" in link or "https" in link:
# b = b'/:?='
# link = urllib.parse.quote(link, b)
# file = urllib.request.urlopen(link)
# print(str(file.getcode()) + "--------" + link)
# urllib.request.urlretrieve(link, "135store/"+str(i) + ".html")
# else:
# url = "http://www.135store.com" + link
# b = b'/:?='
# url = urllib.parse.quote(url,b)
# print(url)
# file = urllib.request.urlopen(url)
# print(str(file.getcode()) + "--------" + link)
# urllib.request.urlretrieve(url, "135store/"+str(i) + ".html")
# i=i+1
| [
"971368174@qq.com"
] | 971368174@qq.com |
4b06a8a723553b2e2d2aa443872d0f1a11769e80 | b426b3aebf0b2ecb27d75d6e64f7e6b34f714877 | /bsm/__init__.py | ef8bf4346e2ee0831c6df964a3e475a799042501 | [
"MIT"
] | permissive | deancolten/buzzsprout-manager | 4c0005f48d27c13e63a8fc4a33f522706f595d36 | a630ee39171b7086ac738e29b721b73c39a1581f | refs/heads/main | 2023-06-02T21:58:49.452025 | 2021-05-14T12:30:42 | 2021-05-14T12:30:42 | 349,064,543 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 101 | py | from bsm.bsm import Manager, Episode, EpisodeGroup
__all__ = ["Manager", "Episode", "EpisodeGroup"]
| [
"coltenrdean@gmail.com"
] | coltenrdean@gmail.com |
a2278d3ee306d014396861ebc2ff06f7c6608467 | 8589f2512eb6e80ca1e35b8b0ba79cd23c97d27f | /input-output/dicom_model/series.py | b6dc512496e40f2a98a0e7b2e04430961aa0cc14 | [] | no_license | lihebi/contrib-pydicom | c1648e37f7d9b53ab4d315604ddc2cb422479cc2 | b847ace414f786d6b6ada2ab3e948ed2ce8dc077 | refs/heads/master | 2022-09-29T18:47:03.987294 | 2020-06-08T14:59:10 | 2020-06-08T14:59:10 | 270,710,118 | 0 | 0 | null | 2020-06-08T14:58:29 | 2020-06-08T14:58:28 | null | UTF-8 | Python | false | false | 2,200 | py | # -*- coding: utf-8 -*-
'''
Dicom Study IOD
'''
from pydicom.config import logger
from image import Image
class Series(object):
def __init__(self, dicom_dataset=None):
self.images = list()
self.dicom_dataset = dicom_dataset
self.images.append(Image(dicom_dataset=dicom_dataset))
def __repr__(self):
try:
output = "\t\tSeriesIUID = %s:\n" % (self.dicom_dataset.SeriesInstanceUID, )
for x in self.images:
output += repr(x)
return output
except Exception as e:
logger.debug("trouble getting Series data", exc_info=e)
return "\t\tSeriesIUID = None\n"
def __str__(self):
try:
return self.dicom_dataset.SeriesInstanceUID
except Exception as e:
logger.debug("trouble getting image SeriesInstanceUID", exc_info=e)
return "None"
def __eq__(self, other):
try:
return self.dicom_dataset.SeriesInstanceUID == other.dicom_dataset.SeriesInstanceUID
except Exception as e:
logger.debug("trouble comparing two Series", exc_info=e)
return False
def __ne__(self, other):
try:
return self.dicom_dataset.SeriesInstanceUID != other.dicom_dataset.SeriesInstanceUID
except Exception as e:
logger.debug("trouble comparing two Series", exc_info=e)
return True
def __getattr__(self, name):
return getattr(self.dicom_dataset, name)
def add_dataset(self, dataset):
try:
if self.dicom_dataset.SeriesInstanceUID == dataset.SeriesInstanceUID:
for x in self.images:
if x.SOPInstanceUID == dataset.SOPInstanceUID:
logger.debug("Image is already part of this series")
break
else:
self.images.append(Image(dicom_dataset=dataset))
else:
raise KeyError("Not the same SeriesInstanceUIDs")
except Exception as e:
logger.debug("trouble adding image to series", exc_info=e)
raise KeyError("Not the same SeriesInstanceUIDs")
| [
"robert.haxton@gmail.com"
] | robert.haxton@gmail.com |
cb5615b463c6b6910cc71d036d3ce614c7746bc3 | eb64d19047431b4a295ee76f9eb49cc5ab88d8ea | /classifier/util.py | 9f3512c21ea8b2f0b90b50c0c24e11f9cff8937c | [] | no_license | glennojmcavoy/team-project | 1cbd5be1dbacd4a25c77978135a728f4107dc136 | 0d469baaf37f22b81abff928ef2d4d2db1430051 | refs/heads/master | 2021-03-02T18:27:55.684139 | 2020-03-05T21:14:15 | 2020-03-05T21:14:15 | 245,893,591 | 0 | 0 | null | 2020-03-08T21:52:15 | 2020-03-08T21:52:15 | null | UTF-8 | Python | false | false | 2,443 | py | from __future__ import absolute_import, division, print_function, unicode_literals
from typing import Tuple
import numpy as np
import tensorflow as tf
import operator
from preprocess.script import PreProcessImages
AUTOTUNE = tf.data.experimental.AUTOTUNE
CLASS_NAMES = np.array(["{:05d}".format(x) for x in range(0, 43)])
def getPredictedLabel(mappedValues):
key = max(mappedValues.items(), key=operator.itemgetter(1))[0]
return key
def predictedLabelToMap(predictedLabel):
mappedLabels = {}
for i in CLASS_NAMES:
mappedLabels[i] = predictedLabel[0][int(i)]
return mappedLabels
def readImageForPrediction(filePath):
img = tf.io.read_file(filePath)
img = tf.image.decode_jpeg(img, channels=3)
img = tf.image.resize_with_crop_or_pad(img, 40, 40)
img = tf.image.convert_image_dtype(img, tf.float64)
return np.asarray(img.numpy()).reshape((1, 40, 40, 3))
def __goBackOneDir(path):
splitOut = path.split("/")
splitOut.remove(splitOut[-2])
out = splitOut[0]
for x in range(1, len(splitOut)):
out = out + "/" + splitOut[x]
return out
def batchResizeAndSplit(inPathRoot: str, outPathRoot: str, trainTestSplit=(80, 20)):
out = __goBackOneDir(outPathRoot)
preprocessor = PreProcessImages(inPathRoot)
preprocessor.batchResize(keepAspectRatio=False, outputTargetSize=(40, 40), outputDirRoot=outPathRoot, outFormat="jpg")
preprocessor.splitDataIntoTrainAndTest(outPathRoot, out, trainTestSplit)
def getDataSet(inPathRoot: str, outPathRoot: str, runPreProcessor=True, trainTestSplit=(80, 20)) -> Tuple[tf.data.Dataset, tf.data.Dataset]:
out = __goBackOneDir(inPathRoot)
if runPreProcessor:
batchResizeAndSplit(inPathRoot, outPathRoot, trainTestSplit)
train = tf.data.Dataset.list_files(out + "train/*/*.jpg")
test = tf.data.Dataset.list_files(out + "test/*/*.jpg")
return train.map(__processPath, num_parallel_calls=AUTOTUNE), test.map(__processPath, num_parallel_calls=AUTOTUNE)
def __getLabel(filePath):
return tf.strings.split(filePath, "/")[-2] == CLASS_NAMES
def __decodeImg(img):
img = tf.image.decode_jpeg(img, channels=3)
img = tf.image.resize_with_crop_or_pad(img, 40, 40)
img = tf.image.convert_image_dtype(img, tf.float64)
return img
def __processPath(filePath):
label = __getLabel(filePath)
img = tf.io.read_file(filePath)
img = __decodeImg(img)
return img, label
| [
"baizelmathew@yahoo.co.in"
] | baizelmathew@yahoo.co.in |
e669d962d439294ded5765ced56d90da42d8611d | c2a8923540268d4eee1851142200a3ebbf828a76 | /tools/EEG_feature_extraction.py | 2f9753c4cbe345000e32598a0ec00a939a60aa3d | [] | no_license | Teresa00/Brain2Speech | 91d4391f4f2b77af8834a29f9f8767cc5cd1682f | 598836713d1787cc47f5bbfaba438681054a351f | refs/heads/master | 2020-03-26T23:09:36.178532 | 2019-05-21T16:31:30 | 2019-05-21T16:31:30 | 145,515,941 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,980 | py | #feature extraction for EEG signal
import math
import numpy as np
import pandas as pd
from scipy.signal import butter, lfilter, freqz, periodogram
from scipy.stats import kurtosis, skew
import scipy.io
import spectrum
import peakutils
import matplotlib.pyplot as plt
from sklearn import preprocessing
import random
########## band-pass filter ##########
def butter_bandpass(lowcut,highcut,fs,order=5):
nyq=0.5*fs
low=lowcut/nyq
high=highcut/nyq
b,a=butter(order,[low,high],btype='band',analog=False)
return b, a
def butter_bandpass_filter(data,lowcut,highcut,fs,order=5):
b, a = butter_bandpass(lowcut,highcut,fs,order=order)
y=lfilter(b,a,data)
return y
##########low-pass filter##########
def butter_lowpass(cutoff,fs,order=5):
nyq=0.5*fs
normal_cutoff=cutoff/nyq
b,a=butter(order,normal_cutoff,btype='low',analog=False)
return b, a
def butter_lowpass_filter(data,cutoff,fs,order=5):
b, a = butter_lowpass(cutoff,fs,order=order)
y=lfilter(b,a,data)
return y
##########high-pass filter##########
def butter_highpass(cutoff,fs,order=5):
nyq=0.5*fs
normal_cutoff=cutoff/nyq
b,a=butter(order,normal_cutoff,btype='high',analog=False)
return b, a
def butter_highpass_filter(data,cutoff,fs,order=5):
b, a = butter_highpass(cutoff,fs,order=order)
y=lfilter(b,a,data)
return y
########## band-stop filter ##########
def butter_bandstop(lowcut,highcut,fs,order=5):
nyq=0.5*fs
low=lowcut/nyq
high=highcut/nyq
b,a=butter(order,[low,high],btype='bandstop',analog=False)
return b, a
def butter_bandstop_filter(data,lowcut,highcut,fs,order=5):
b, a = butter_bandstop(lowcut,highcut,fs,order=order)
y=lfilter(b,a,data)
return y
def EEG_filter_band(signal, fs=1024):
'''
INPUT: EEG signal (Recommend: after removing mean value and other noise filtering method)
The default value of sampling frequency is 1024Hz (For the letter/pseudo-letter dataset.)
RETURN: A dictionary including 6 frequency band EEG signal
'''
theta = butter_bandpass_filter(signal, 4, 7, fs)
alpha = butter_bandpass_filter(signal, 8, 13, fs)
beta_1 = butter_bandpass_filter(signal, 14, 24, fs)
beta_2 = butter_bandpass_filter(signal, 25, 35, fs)
gamma_1 = butter_bandpass_filter(signal, 36, 58, fs)
gamma_2 = butter_bandpass_filter(signal, 62, 100, fs)
return{'theta':theta, 'alpha':alpha, 'beta1':beta_1, 'beta2':beta_2, 'gamma1':gamma_1, 'gamma2':gamma_2}
# a 30Hz lowpass filter
def EEG_lowpass(signal, fs=1024):
filtered_EEG = butter_lowpass_filter(signal, 30, fs)
return filtered_EEG
# filter out 54-66 Hz noise
def EEG_bandstop(signal, fs=1024):
filtered_EEG = butter_bandstop_filter(signal, 54, 66, fs)
return filtered_EEG
# normalize the signal to zero mean and unit variance
def standarization(signal):
return preprocessing.scale(signal)
# normalize to scale [a,b]
def nomalization(data,a,b):
#data is a ndarray
max_diff = max(data) - min(data)
diff = data - min(data)
return a+((b-a)*diff/max_diff)
def zero_mean(signal):
# signal is EEG signal (a vector)
return signal - np.mean(signal)
def EEG_mean(signal):
return np.mean(signal)
def EEG_std(signal):
return np.std(signal)
def EEG_kurtosis(signal):
return scipy.stats.kurtosis(signal,bias=False)
def EEG_skewness(signal):
return scipy.stats.skew(signal,bias=False)
# return the amplitude envelope of hilbert analytical signal
def hilbert(signal):
return abs(scipy.signal.hilbert(signal))
def Feature_Extraction(signal, fs = 1024):
#This function reads the data (x) and the sampling frequency fs, and extracts the feature-vectors(y)
#x should be a vector (1 * N), and y is a row-vctor whose elements are the features
#Mean
#sig_mean = np.mean(signal)
#STD - standard deviation
#sig_std = np.std(signal)
# If we normalize the signal to zero mean and unit variance,
# then we do not need to compute these two features
# Signal Power (after standarization ! otherwise the value will be too big)
# sig_power = np.mean(np.square(signal))
#Kurtosis
Kseg = scipy.stats.kurtosis(signal,bias=False)
#Skewness
Sseg = scipy.stats.skew(signal,bias=False)
Feature = np.array([Kseg, Sseg])
#Feature = np.array([sig_mean, sig_std])
return Feature
#return a numpy array row-vector
def generate_feature_data(subject_name, channel_number):
data_dir = "/Users/teresazhao/Desktop/summer-project/"+subject_name+"/"+subject_name+"_channel"
# + number_of_cannel + ".csv"
# only use one channel data
data = pd.read_csv(data_dir+str(channel_number)+".csv")
# read data from all channel (1-66)
#for j in range(2,67):
# new_data = pd.read_csv(data_dir + str(j) +".csv")
# data = pd.concat([data, new_data], axis=1)
data_T = data.T
#the last column are the labels (0-letter; 1-pseudo-letter)
nrow = data_T.shape[0]
ncol = data_T.shape[1]
#the first line (row) should be discarded (get [1:231] lines)
data_trim = data_T[1:nrow]
X = data_trim.drop(ncol-1,axis=1)
Y = data_trim[[ncol-1]]
X=np.array(X)
Y=np.array(Y)
X_feature = list()
X_cut = X[:, 512:872]
#normalization and band filtering
for i in range(X.shape[0]):
#X[i] = EEG_filter_band(X[i])['theta']
#X[i] = EEG_filter_band(X[i])['alpha']
#X[i] = EEG_filter_band(X[i])['beta1']
#X[i] = EEG_filter_band(X[i])['beta2']
#X[i] = EEG_filter_band(X[i])['gamma1']
#X[i] = EEG_filter_band(X[i])['gamma2']
#X_cut[i] = standarization(X_cut[i])
#X_cut[i] = nomalization(X_cut[i], -1, 1)
X_feature.append(Feature_Extraction(X_cut[i]))
X = np.array(X_feature)
# convert data to feature vectors (N * 2 numpy array)
# disorganize the data to split to training and testing set
index = [i for i in range(len(X))]
random.shuffle(index)
X = X[index]
Y = Y[index]
num_train = round(0.8 * X.shape[0])
X_train = X[0 : (num_train-1)]
Y_train = Y[0 : (num_train-1)]
X_test = X[num_train : (X.shape[0]-1)]
Y_test = Y[num_train : (X.shape[0]-1)]
return {"X_train":X_train, "Y_train": Y_train,"X_test": X_test, "Y_test": Y_test}
| [
"noreply@github.com"
] | noreply@github.com |
8fcbfa32a3cb2aab4ae34e37e6ff4a569f55b5ae | 41c0cbfbe922f09df9c6a4237c06a28ef458761c | /1278 B hyper set .py | 293d5e43a6792d28080181b20ec02d965484ab1d | [] | no_license | adityachaudhary147/py-codes | 8d45bfe3d3b67e4e802a2c1e01199551ef226aaa | 6a8918b5c6fca19ff74cef0dcd676c04b28ee8c4 | refs/heads/master | 2023-04-12T09:52:09.622458 | 2021-05-17T07:44:29 | 2021-05-17T07:44:29 | 244,203,716 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 615 | py | #jai mata di#
import sys
sys.stdin = open('input.in', 'r')
sys.stdout = open('output.out', 'w')
#start the code from here
n,k2=map(int,input().split())
l=set()
for i in range(n):
e=input()
l.add(e)
r=set(l)
l=list(l)
an=0
for j in range(n-1):
for k in range(j+1,n):
we=[0]*k2
ty=0
while ty<k2:
if l[j][ty]==l[k][ty]:
we[ty]=l[j][ty]
else:
if l[j][ty]!='E' and l[k][ty]!='E':
we[ty]='E'
if l[j][ty]!='T' and l[k][ty]!='T':
we[ty]='T'
if l[j][ty]!='S' and l[k][ty]!='S':
we[ty]='S'
ty+=1
we=''.join(we)
# print(we)
if we in r:
an+=1
print(an//3)
| [
"chaudharyaditya.in@gmail.com"
] | chaudharyaditya.in@gmail.com |
611277648f1bcbd7353801bb9f43e68a9bfc7fcb | f68b2d9c6d6a7b7b7a4ae41dbccef33f9cf53b09 | /conta.py | 3ebebb126903ba34e7f17f2445f33ee168739f82 | [] | no_license | gabrieldfm/OoPython | 1d56b9a41f1fe91bb73431f86bdece1568275e26 | 0a54797a6d833392a229e1a201d5c935d7e9c178 | refs/heads/master | 2020-04-02T16:10:55.678381 | 2018-10-26T12:54:57 | 2018-10-26T12:54:57 | 154,601,747 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,128 | py | class Conta:
def __init__(self, numero, titular, saldo, limite):
print("construindo")
self.__numero = numero
self.__titular = titular
self.__saldo = saldo
self.__limite = limite
def extrato(self):
print("Saldo {} do titular {}".format(self.__saldo, self.__titular))
def deposita(self, valor):
self.__saldo += valor
def __pode_sacar(self, valor_a_sacar):
valor_disponivel = self.__saldo + self.__limite
return valor_a_sacar <= valor_disponivel
def saca(self, valor):
if(self.__pode_sacar()):
self.__saldo -= valor
else:
print("O valor passou o limite")
def transfere(self, valor, destino):
self.saca(valor)
destino.saca(valor)
@property
def saldo(self):
return self.__saldo
@property
def titular(self):
return self.__titular
@property
def limite(self):
return self.__limite
@limite.setter
def set_limite(self, valor):
self.__limite = valor
@staticmethod
def codigo_banco():
return "001" | [
"gabrieldfm13@gmail.com"
] | gabrieldfm13@gmail.com |
f3ffe16193b8c35ccd8106ff5a0ab559afb208d7 | 2235ad0c5005ffda352792b477b163bb52085888 | /univ_cc/univ_cc/spiders/univ_spider.py | 44850f8d37a5efa661daf961ae6d544e3eb428e3 | [] | no_license | reedknight/scrap-world-university-url | 245ec5f09f7aed8d50a401e5371a4150f90ec96e | e3017273a46520f7d1f7972da4e6271f70848775 | refs/heads/master | 2021-01-25T04:22:26.647032 | 2017-06-05T18:37:52 | 2017-06-05T18:37:52 | 93,434,669 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,747 | py | import scrapy
from pprint import pprint
class UnivSpider(scrapy.Spider):
name = "univ"
def start_requests(self):
urls = [
'https://univ.cc/world.php'
]
for url in urls:
yield scrapy.Request(url=url, callback=self.parse)
def parse(self, response):
for country in response.css("select option"):
code = country.xpath("@value").extract()[0]
if code == "world":
continue
country = country.xpath("text()").extract()[0]
url = "https://univ.cc/search.php?start=1&dom=" + str(code)
request = scrapy.Request(url=url, callback=self.parse_univ)
request.meta['country'] = {
'name' : country.encode('utf8'),
'code' : code.encode('utf8'),
'search_url' : url,
'universities' : [],
}
yield request
def parse_univ(self, response):
for university in response.css("ol li a"):
name = university.xpath("text()").extract()[0]
url = university.xpath("@href").extract()[0]
response.meta['country']['universities'].append({
'name' : name.encode('utf8'),
'url' : url.encode('utf8'),
})
link_url = response.xpath("//nav[@class='resultNavigation']/a[text()=' [>>Next]']/@href").extract()
if len(link_url) > 0:
self.logger.info("VISITING NEXT LINK : %s", str(link_url))
request = response.follow("https://univ.cc/" + link_url[0], self.parse_univ)
request.meta['country'] = response.meta['country']
yield request
else:
yield response.meta['country']
| [
"reedknight@localhost.localdomain"
] | reedknight@localhost.localdomain |
244e9640fdeb86e1e6c96196b3bcbd80dfd52f5a | 6f518d374055a86c87081cd66098cda1d4f99869 | /mdsys_struct.py | 205bf4d4744c3ae35ea2f391b894936fa8a9af15 | [] | no_license | davydenk/Team_one_superproject | 4b011ead6a4d36d12f40c62545fcdce26b752b97 | 1fe28a29cb6f77cca554762cdcbe4846635085cc | refs/heads/master | 2022-04-04T04:07:37.760306 | 2020-02-20T17:02:34 | 2020-02-20T17:02:34 | 240,039,595 | 0 | 0 | null | 2020-02-19T16:46:29 | 2020-02-12T14:57:19 | C | UTF-8 | Python | false | false | 4,948 | py | import ctypes as ct
import sys
so_ljmd = "../lib/ljmd.so"
c_ljmd = ct.CDLL(so_ljmd,ct.RTLD_GLOBAL)
def wrap_function(lib, funcname, restype, argtypes):
"""Simplify wrapping ctypes functions"""
func = lib.__getattr__(funcname)
func.restype = restype
func.argtypes = argtypes
return func
#reads a line from an open file and strips comments
def get_a_line(ifile) :
line = ifile.readline()
line = line.partition('#')[0]
line = line.rstrip()
return line
class mdsys(ct.Structure):
_fields_ = [ ("natoms", ct.c_int), ("nfi", ct.c_int), ("nsteps",ct.c_int), ("rank", ct.c_int), ("nps", ct.c_int),
("dt",ct.c_double), ("mass",ct.c_double), ("epsilon", ct.c_double), ("sigma", ct.c_double), ("box", ct.c_double), ("rcut", ct.c_double), ("ekin", ct.c_double), ("epot", ct.c_double), ("temp", ct.c_double), ("rx", ct.POINTER(ct.c_double)), ("ry", ct.POINTER(ct.c_double)), ("rz", ct.POINTER(ct.c_double)), ("vx", ct.POINTER(ct.c_double)), ("vy", ct.POINTER(ct.c_double)), ("vz", ct.POINTER(ct.c_double)), ("fx", ct.POINTER(ct.c_double)),("fy", ct.POINTER(ct.c_double)), ("fz", ct.POINTER(ct.c_double)), ("cx", ct.POINTER(ct.c_double)),("cy", ct.POINTER(ct.c_double)), ("cz", ct.POINTER(ct.c_double)) ]
def __init__(self):
self.nfi=0
self.force_func = wrap_function(c_ljmd, 'force', None, [ct.POINTER(mdsys)])
self.ekin_func = wrap_function(c_ljmd, 'ekin', None, [ct.POINTER(mdsys)])
self.vel1_func = wrap_function(c_ljmd, 'vel_step1', None, [ct.POINTER(mdsys)])
self.vel2_func = wrap_function(c_ljmd, 'vel_step2', None, [ct.POINTER(mdsys)])
self.broadcast_vals_func = wrap_function(c_ljmd, 'broadcast_values', None, [ct.POINTER(mdsys)])
self.broadcast_arrs_func = wrap_function(c_ljmd, 'broadcast_arrays', None, [ct.POINTER(mdsys)])
self.extra_alloc_func = wrap_function(c_ljmd, 'allocate_cs', None, [ct.POINTER(mdsys)])
self.extra_free_func = wrap_function(c_ljmd, 'free_cs', None, [ct.POINTER(mdsys)])
self.get_rank_nps_func = wrap_function(c_ljmd, 'get_rank_nps', None, [ct.POINTER(mdsys)])
def force(self):
self.force_func(self)
#trying to stick close to c code, but can't keep both function and field with the same name
def ekin_f(self):
self.ekin_func(self)
def vel1(self):
self.vel1_func(self)
def vel2(self):
self.vel2_func(self)
def get_rank_nps(self):
self.get_rank_nps_func(self)
def broadcast_arrs(self):
self.broadcast_arrs_func(self)
def broadcast_vals(self):
self.broadcast_vals_func(self)
def extra_alloc(self):
self.extra_alloc_func(self)
def extra_free(self):
self.extra_free_func(self)
def alloc_ptrs(self):
self.rx = (ct.c_double * self.natoms)()
self.ry = (ct.c_double * self.natoms)()
self.rz = (ct.c_double * self.natoms)()
self.vx = (ct.c_double * self.natoms)()
self.vy = (ct.c_double * self.natoms)()
self.vz = (ct.c_double * self.natoms)()
self.fx = (ct.c_double * self.natoms)()
self.fy = (ct.c_double * self.natoms)()
self.fz = (ct.c_double * self.natoms)()
def read_input(self):
if self.rank==0:
with sys.stdin as input_file:
self.natoms=int(get_a_line(input_file));
self.mass=float(get_a_line(input_file));
self.epsilon=float(get_a_line(input_file));
self.sigma=float(get_a_line(input_file));
self.rcut=float(get_a_line(input_file));
self.box=float(get_a_line(input_file));
self.restfile=get_a_line(input_file)
self.trajfile=get_a_line(input_file)
self.ergfile=get_a_line(input_file)
self.nsteps=int(get_a_line(input_file));
self.dt=float(get_a_line(input_file));
self.nprint=int(get_a_line(input_file));
self.ergf=open(self.ergfile,'w')
self.trajf=open(self.trajfile,'w')
def fill_pos_vel(self):
with open(self.restfile,'r') as input_file:
for i in range(self.natoms):
line = input_file.readline()
val_list = [float(j) for j in line.split()]
self.rx[i]= val_list[0]
self.ry[i]=val_list[1]
self.rz[i]=val_list[2]
for i in range(self.natoms):
line = input_file.readline()
val_list = [float(j) for j in line.split()]
self.vx[i]=val_list[0]
self.vy[i]=val_list[1]
self.vz[i]=val_list[2]
def output(self):
print("% 8d % 20.8f % 20.8f % 20.8f % 20.8f\n" % (self.nfi, self.temp, self.ekin, self.epot, self.ekin+self.epot));
self.ergf.write("% 8d % 20.8f % 20.8f % 20.8f % 20.8f\n" % (self.nfi, self.temp, self.ekin, self.epot, self.ekin+self.epot));
self.trajf.write("%d\n nfi=%d etot=%20.8f\n"% (self.natoms, self.nfi, self.ekin+self.epot));
for i in range(self.natoms):
self.trajf.write("Ar %20.8f %20.8f %20.8f\n" % (self.rx[i], self.ry[i], self.rz[i]));
def close_files(self):
self.ergf.close()
self.trajf.close()
| [
"cnhtkf@yandex.ru"
] | cnhtkf@yandex.ru |
5df0e289cc5dcf1194ecb88276405c9e5994cc51 | 3fa96b02cdc4d70f9ef85e69dc79626aee5d8db9 | /catkin_ws/src/hiq_racecar/include/hiq_racecar/calibration.py | 43d88fe469f162cd69d531fcc6dd29985707951b | [] | no_license | adamvlang/AD17 | 721754b1941e7be8ea0ae1dca61232c38981e50e | 8a022966918b55b90b46c795ecc25702d97f30e8 | refs/heads/master | 2021-06-23T21:10:01.078571 | 2019-08-20T16:43:34 | 2019-08-20T16:43:34 | 109,722,994 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,575 | py | #!/usr/bin/env python
import matplotlib.pyplot as Plt
import cv2
import pickle
from chessboard import ChessBoard
from helpers import show_images, save_image
# Let's initialize 20 chessboards
# note that at instatiation, it finds all chessboard corners and object points
chessboards = []
for n in range(20):
this_path = 'camera_cal/calibration' + str(n + 1) + '.png'
chessboard = ChessBoard(i = n, path = this_path, nx = 9, ny = 6)
chessboards.append(chessboard)
# We use these corners and object points (and image dimension)
# from all the chessboards to calculate the calibration parameters
points, corners, shape = [], [], chessboards[0].dimensions
for chessboard in chessboards:
if chessboard.has_corners:
points.append(chessboard.object_points)
corners.append(chessboard.corners)
r, matrix, distortion_coef, rv, tv = cv2.calibrateCamera(points, corners, shape, None, None)
# Let's store these camera calibration parameters somewhere else so we can use it later
calibration_data = {
"camera_matrix": matrix,
"distortion_coefficient": distortion_coef
}
pickle.dump(calibration_data, open( "calibration_data.p", "wb" ))
# Let's load the camera calibration parameters to each chessboard as additional detail
# If we don't do this, we won't be able to get an undistorted image from that instance
for chessboard in chessboards:
chessboard.load_undistort_params(camera_matrix = matrix, distortion = distortion_coef)
# Save each image to respective files
for chessboard in chessboards:
if chessboard.has_corners:
save_image(chessboard.image_with_corners(), "corners", chessboard.i)
if chessboard.can_undistort:
save_image(chessboard.undistorted_image(), "undistortedboard", chessboard.i)
# Visualization
raw_images, images_with_corners, undistorted_images = [], [], []
for chessboard in chessboards:
raw_images.append(chessboard.image())
if chessboard.has_corners:
images_with_corners.append(chessboard.image_with_corners())
if chessboard.can_undistort:
undistorted_images.append(chessboard.undistorted_image())
show_images(raw_images, per_row=5, per_col=4, W=10, H=5)
show_images(images_with_corners, per_row=6, per_col=3, W=12, H=4)
show_images(undistorted_images, per_row=5, per_col=4, W=10, H=5)
# Uncomment lines below for larger visualization
# show_images(raw_images, per_row = 3, per_col = 7, W = 15, H = 20)
# show_images(images_with_corners, per_row = 3, per_col = 6, W = 15, H = 18)
# show_images(undistorted_images, per_row = 3, per_col = 7, W = 13, H = 18)
| [
"jetson@tx2.com"
] | jetson@tx2.com |
9942d801f6cc00ba6d986b99d720d52d4c983f1e | 5fff533c22c2a166c57a8e78ad1a6fd44dcb14a6 | /algorithms/sorting/bubble_sort.py | 98e209735176d0c8d9d5e6238955fa3c6b1d4cdb | [
"Apache-2.0"
] | permissive | emirot/algo-loco.blog | 5d6a28fa28dd2c3eb6c22ebcae16652bd6cba8ae | 0e377a7b0699ded1599a9a5025148236bc2ee22a | refs/heads/master | 2020-07-02T22:26:31.596802 | 2020-06-29T01:11:27 | 2020-06-29T01:11:27 | 201,687,270 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 392 | py | def bubble_sort(arr):
unsort = True
while unsort:
unsort = False
for i in range(1, len(arr)):
if arr[i - 1] > arr[i]:
arr[i - 1], arr[i] = arr[i], arr[i - 1]
unsort = True
return arr
if __name__ == "__main__":
arr = [3, 2, 10, 1, 4, 5]
res = bubble_sort(arr)
assert bubble_sort(arr) == [1, 2, 3, 4, 5, 10]
| [
"emirot.nolan@gmail.com"
] | emirot.nolan@gmail.com |
09360acd5784b7d43b7da742def5c650aacf37dc | 38a972a3cd1fc303b5f877e24d65118912d85d1c | /path/to/virtualenv/project/Lib/site-packages/tensorflow/python/ops/accumulate_n_benchmark.py | 9fb5b537c24e331fe9de5436d162df1024bcb89b | [] | no_license | ZulfikarAkbar/YOLO_ObjectDetection | 0c1015aa987d03329eae48a2053a07dda05d96c0 | 3517d0592a269f79df9afd82e0b1b0123bbe0473 | refs/heads/master | 2022-10-27T05:08:26.734173 | 2019-02-07T17:35:22 | 2019-02-07T17:35:22 | 169,613,306 | 0 | 1 | null | 2022-10-18T02:18:17 | 2019-02-07T17:35:03 | Python | UTF-8 | Python | false | false | 5,606 | py | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Benchmark for accumulate_n() in math_ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import random
import time
from six.moves import xrange # pylint: disable=redefined-builtin
from tensorflow.python.client import session
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import data_flow_ops
from tensorflow.python.ops import gen_control_flow_ops
from tensorflow.python.ops import gen_state_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import state_ops
from tensorflow.python.platform import test
class AccumulateNBenchmark(test.Benchmark):
def _AccumulateNTemplate(self, inputs, init, shape, validate_shape):
var = gen_state_ops.temporary_variable(
shape=shape, dtype=inputs[0].dtype.base_dtype)
ref = state_ops.assign(var, init, validate_shape=validate_shape)
update_ops = [
state_ops.assign_add(
ref, tensor, use_locking=True).op for tensor in inputs
]
with ops.control_dependencies(update_ops):
return gen_state_ops.destroy_temporary_variable(ref, var_name=var.op.name)
def _AccumulateNInitializedWithFirst(self, inputs):
return self._AccumulateNTemplate(
inputs,
init=array_ops.zeros_like(inputs[0]),
shape=inputs[0].get_shape(),
validate_shape=True)
def _AccumulateNInitializedWithMerge(self, inputs):
return self._AccumulateNTemplate(
inputs,
init=array_ops.zeros_like(gen_control_flow_ops.merge(inputs)[0]),
shape=tensor_shape.vector(0),
validate_shape=False)
def _AccumulateNInitializedWithShape(self, inputs):
return self._AccumulateNTemplate(
inputs,
init=array_ops.zeros(
shape=inputs[0].get_shape(), dtype=inputs[0].dtype.base_dtype),
shape=inputs[0].get_shape(),
validate_shape=True)
def _GenerateUnorderedInputs(self, size, n):
inputs = [random_ops.random_uniform(shape=[size]) for _ in xrange(n)]
random.shuffle(inputs)
return inputs
def _GenerateReplicatedInputs(self, size, n):
return n * self._GenerateUnorderedInputs(size, 1)
def _GenerateOrderedInputs(self, size, n):
inputs = self._GenerateUnorderedInputs(size, 1)
queue = data_flow_ops.FIFOQueue(
capacity=1, dtypes=[inputs[0].dtype], shapes=[inputs[0].get_shape()])
for _ in xrange(n - 1):
op = queue.enqueue(inputs[-1])
with ops.control_dependencies([op]):
inputs.append(math_ops.tanh(1.0 + queue.dequeue()))
return inputs
def _GenerateReversedInputs(self, size, n):
inputs = self._GenerateOrderedInputs(size, n)
inputs.reverse()
return inputs
def _SetupAndRunBenchmark(self, graph, inputs, repeats, format_args):
with graph.as_default():
add_n = math_ops.add_n(inputs)
acc_n_first = self._AccumulateNInitializedWithFirst(inputs)
acc_n_merge = self._AccumulateNInitializedWithMerge(inputs)
acc_n_shape = self._AccumulateNInitializedWithShape(inputs)
test_ops = (("AddN", add_n.op),
("AccNFirst", acc_n_first.op),
("AccNMerge", acc_n_merge.op),
("AccNShape", acc_n_shape.op))
with session.Session(graph=graph):
for tag, op in test_ops:
for _ in xrange(100):
op.run() # Run for warm up.
start = time.time()
for _ in xrange(repeats):
op.run()
duration = time.time() - start
args = format_args + (tag, duration)
print(self._template.format(*args))
def _RunBenchmark(self, tag, input_fn, sizes, ninputs, repeats):
for size in sizes:
for ninput in ninputs:
graph = ops.Graph()
with graph.as_default():
inputs = input_fn(size, ninput)
format_args = (tag, size, ninput, repeats)
self._SetupAndRunBenchmark(graph, inputs, repeats, format_args)
def benchmarkAccumulateN(self):
self._template = "{:<15}" * 6
args = {
"sizes": (128, 128**2),
"ninputs": (1, 10, 100, 300),
"repeats": 100
}
benchmarks = (("Replicated", self._GenerateReplicatedInputs),
("Unordered", self._GenerateUnorderedInputs),
("Ordered", self._GenerateOrderedInputs),
("Reversed", self._GenerateReversedInputs))
print(self._template.format("", "Size", "#Inputs", "#Repeat", "Method",
"Duration"))
print("-" * 90)
for benchmark in benchmarks:
self._RunBenchmark(*benchmark, **args)
if __name__ == "__main__":
test.main()
| [
"zulfikar.78.akbar@gmail.com"
] | zulfikar.78.akbar@gmail.com |
46bd355b248024f119ca45c907849962f98b6742 | ed9ffe9aa1a173373d53d68c8e19103f656c3eb6 | /src/home/authentication.py | f4298d1521637a3247729cc9fd3451378646c72c | [] | no_license | shigengyu/Genghis | 7dd00db57db93b3db3862ad943a3fe0ef0b19685 | 5cf770c2f09ad23204fe43518704731429a68918 | refs/heads/master | 2021-01-02T09:02:00.776175 | 2014-04-20T16:50:30 | 2014-04-20T16:50:30 | 10,285,549 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 2,090 | py | from django.http.response import HttpResponseRedirect, HttpResponseForbidden
from django.contrib.auth.models import User
from social_auth.models import UserSocialAuth
from genghis.settings import ADMINS, GENGHIS_ENVIRONMENT
class require_login(object):
def __init__(self, func):
self.func = func
def __call__(self, *args, **kwargs):
if (not self.instance.request.user.is_authenticated()):
return_url = self.instance.request.get_full_path()
return HttpResponseRedirect('/home/login?next=' + return_url)
result = self.func.__call__(self.instance, *args, **kwargs)
return result
def __get__(self, instance, owner):
self.owner = owner
self.instance = instance
return self.__call__
class require_admin(require_login):
def __call__(self, *args, **kwargs):
if (not is_admin(self.instance.request.user)):
return HttpResponseForbidden()
result = self.func.__call__(self.instance, *args, **kwargs)
return result
def is_admin(user):
authenticated = user.is_authenticated()
is_superuser = user.is_superuser or GENGHIS_ENVIRONMENT == 'dev'
return authenticated and is_superuser
def populate_is_admin(request):
user = request.user
authenticated = hasattr(user, 'is_authenticated') and user.is_authenticated()
is_admin = authenticated and request.user.is_superuser
if GENGHIS_ENVIRONMENT == 'dev':
is_admin = authenticated
return {'is_admin': is_admin, 'environment': GENGHIS_ENVIRONMENT }
def populate_social_auth_backend(request):
associated = None
associated_name = None
user = request.user
if hasattr(user, 'is_authenticated') and user.is_authenticated():
associated = UserSocialAuth.get_social_auth_for_user(user)
if associated:
for name in ['Google', 'Facebook', 'Linkedin', 'Flickr']:
if name in str(associated):
associated_name = name
break;
return {'associated_auth_backend': associated_name } | [
"univer.shi@gmail.com"
] | univer.shi@gmail.com |
7e3c2e7d4ca119e95ec521975f9d6d5b3fae2f5d | 7c90b43f5f1da150a356b80af22f0c5ae62d9f28 | /lib/PartitionData.py | baa001b0dbdfb3bc075d3b88b6964265e8e9f4ef | [
"LicenseRef-scancode-other-permissive"
] | permissive | prajwal309/TierraCrossSection | 8693de020f080deb989a6937cc9b296ebc6511ec | 4b8896540616ec3f46322ea4c40df9f2b4774a44 | refs/heads/main | 2023-01-04T14:45:12.370217 | 2020-11-05T19:38:52 | 2020-11-05T19:38:52 | 310,383,980 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 613,051 | py | from numpy import complex128,int64,float64,float32
__ComplexType__ = complex128
__IntegerType__ = int64
__FloatType__ = float64
TIPS_2017_ISOT = {}
TIPS_2017_ISOT = {}
TIPS_2017_ISOT[0] = float64([
1.0, 20.0, 40.0, 60.0, 80.0, 100.0, 120.0, 140.0, 160.0, 180.0,
200.0, 220.0, 240.0, 260.0, 280.0, 300.0, 320.0, 340.0, 360.0, 380.0,
400.0, 420.0, 440.0, 460.0, 480.0, 500.0, 520.0, 540.0, 560.0, 580.0,
600.0, 620.0, 640.0, 660.0, 680.0, 700.0, 720.0, 740.0, 760.0, 780.0,
800.0, 820.0, 840.0, 860.0, 880.0, 900.0, 920.0, 940.0, 960.0, 980.0,
1000.0, 1020.0, 1040.0, 1060.0, 1080.0, 1100.0, 1120.0, 1140.0, 1160.0, 1180.0,
1200.0, 1220.0, 1240.0, 1260.0, 1280.0, 1300.0, 1320.0, 1340.0, 1360.0, 1380.0,
1400.0, 1420.0, 1440.0, 1460.0, 1480.0, 1500.0, 1520.0, 1540.0, 1560.0, 1580.0,
1600.0, 1620.0, 1640.0, 1660.0, 1680.0, 1700.0, 1720.0, 1740.0, 1760.0, 1780.0,
1800.0, 1820.0, 1840.0, 1860.0, 1880.0, 1900.0, 1920.0, 1940.0, 1960.0, 1980.0,
2000.0, 2020.0, 2040.0, 2060.0, 2080.0, 2100.0, 2120.0, 2140.0, 2160.0, 2180.0,
2200.0, 2220.0, 2240.0, 2260.0, 2280.0, 2300.0, 2320.0, 2340.0, 2360.0, 2380.0,
2400.0, 2420.0, 2440.0, 2460.0, 2480.0, 2500.0, 2520.0, 2540.0, 2560.0, 2580.0,
2600.0, 2620.0, 2640.0, 2660.0, 2680.0, 2700.0, 2720.0, 2740.0, 2760.0, 2780.0,
2800.0, 2820.0, 2840.0, 2860.0, 2880.0, 2900.0, 2920.0, 2940.0, 2960.0, 2980.0,
3000.0, 3020.0, 3040.0, 3060.0, 3080.0, 3100.0, 3120.0, 3140.0, 3160.0, 3180.0,
3200.0, 3220.0, 3240.0, 3260.0, 3280.0, 3300.0, 3320.0, 3340.0, 3360.0, 3380.0,
3400.0, 3420.0, 3440.0, 3460.0, 3480.0, 3500.0, 3520.0, 3540.0, 3560.0, 3580.0,
3600.0, 3620.0, 3640.0, 3660.0, 3680.0, 3700.0, 3720.0, 3740.0, 3760.0, 3780.0,
3800.0, 3820.0, 3840.0, 3860.0, 3880.0, 3900.0, 3920.0, 3940.0, 3960.0, 3980.0,
4000.0, 4020.0, 4040.0, 4060.0, 4080.0, 4100.0, 4120.0, 4140.0, 4160.0, 4180.0,
4200.0, 4220.0, 4240.0, 4260.0, 4280.0, 4300.0, 4320.0, 4340.0, 4360.0, 4380.0,
4400.0, 4420.0, 4440.0, 4460.0, 4480.0, 4500.0, 4520.0, 4540.0, 4560.0, 4580.0,
4600.0, 4620.0, 4640.0, 4660.0, 4680.0, 4700.0, 4720.0, 4740.0, 4760.0, 4780.0,
4800.0, 4820.0, 4840.0, 4860.0, 4880.0, 4900.0, 4920.0, 4940.0, 4960.0, 4980.0,
5000.0,
])
TIPS_2017_ISOT[1] = float64([
1.0, 20.0, 40.0, 60.0, 80.0, 100.0, 120.0, 140.0, 160.0, 180.0,
200.0, 220.0, 240.0, 260.0, 280.0, 300.0, 320.0, 340.0, 360.0, 380.0,
400.0, 420.0, 440.0, 460.0, 480.0, 500.0, 520.0, 540.0, 560.0, 580.0,
600.0, 620.0, 640.0, 660.0, 680.0, 700.0, 720.0, 740.0, 760.0, 780.0,
800.0, 820.0, 840.0, 860.0, 880.0, 900.0, 920.0, 940.0, 960.0, 980.0,
1000.0, 1020.0, 1040.0, 1060.0, 1080.0, 1100.0, 1120.0, 1140.0, 1160.0, 1180.0,
1200.0, 1220.0, 1240.0, 1260.0, 1280.0, 1300.0, 1320.0, 1340.0, 1360.0, 1380.0,
1400.0, 1420.0, 1440.0, 1460.0, 1480.0, 1500.0, 1520.0, 1540.0, 1560.0, 1580.0,
1600.0, 1620.0, 1640.0, 1660.0, 1680.0, 1700.0, 1720.0, 1740.0, 1760.0, 1780.0,
1800.0, 1820.0, 1840.0, 1860.0, 1880.0, 1900.0, 1920.0, 1940.0, 1960.0, 1980.0,
2000.0, 2020.0, 2040.0, 2060.0, 2080.0, 2100.0, 2120.0, 2140.0, 2160.0, 2180.0,
2200.0, 2220.0, 2240.0, 2260.0, 2280.0, 2300.0, 2320.0, 2340.0, 2360.0, 2380.0,
2400.0, 2420.0, 2440.0, 2460.0, 2480.0, 2500.0, 2520.0, 2540.0, 2560.0, 2580.0,
2600.0, 2620.0, 2640.0, 2660.0, 2680.0, 2700.0, 2720.0, 2740.0, 2760.0, 2780.0,
2800.0, 2820.0, 2840.0, 2860.0, 2880.0, 2900.0, 2920.0, 2940.0, 2960.0, 2980.0,
3000.0, 3020.0, 3040.0, 3060.0, 3080.0, 3100.0, 3120.0, 3140.0, 3160.0, 3180.0,
3200.0, 3220.0, 3240.0, 3260.0, 3280.0, 3300.0, 3320.0, 3340.0, 3360.0, 3380.0,
3400.0, 3420.0, 3440.0, 3460.0, 3480.0, 3500.0, 3520.0, 3540.0, 3560.0, 3580.0,
3600.0, 3620.0, 3640.0, 3660.0, 3680.0, 3700.0, 3720.0, 3740.0, 3760.0, 3780.0,
3800.0, 3820.0, 3840.0, 3860.0, 3880.0, 3900.0, 3920.0, 3940.0, 3960.0, 3980.0,
4000.0, 4020.0, 4040.0, 4060.0, 4080.0, 4100.0, 4120.0, 4140.0, 4160.0, 4180.0,
4200.0, 4220.0, 4240.0, 4260.0, 4280.0, 4300.0, 4320.0, 4340.0, 4360.0, 4380.0,
4400.0, 4420.0, 4440.0, 4460.0, 4480.0, 4500.0, 4520.0, 4540.0, 4560.0, 4580.0,
4600.0, 4620.0, 4640.0, 4660.0, 4680.0, 4700.0, 4720.0, 4740.0, 4760.0, 4780.0,
4800.0, 4820.0, 4840.0, 4860.0, 4880.0, 4900.0, 4920.0, 4940.0, 4960.0, 4980.0,
5000.0, 5020.0, 5040.0, 5060.0, 5080.0, 5100.0, 5120.0, 5140.0, 5160.0, 5180.0,
5200.0, 5220.0, 5240.0, 5260.0, 5280.0, 5300.0, 5320.0, 5340.0, 5360.0, 5380.0,
5400.0, 5420.0, 5440.0, 5460.0, 5480.0, 5500.0, 5520.0, 5540.0, 5560.0, 5580.0,
5600.0, 5620.0, 5640.0, 5660.0, 5680.0, 5700.0, 5720.0, 5740.0, 5760.0, 5780.0,
5800.0, 5820.0, 5840.0, 5860.0, 5880.0, 5900.0, 5920.0, 5940.0, 5960.0, 5980.0,
6000.0,
])
TIPS_2017_ISOT[2] = float64([
1.0, 20.0, 40.0, 60.0, 80.0, 100.0, 120.0, 140.0, 160.0, 180.0,
200.0, 220.0, 240.0, 260.0, 280.0, 300.0, 320.0, 340.0, 360.0, 380.0,
400.0, 420.0, 440.0, 460.0, 480.0, 500.0, 520.0, 540.0, 560.0, 580.0,
600.0, 620.0, 640.0, 660.0, 680.0, 700.0, 720.0, 740.0, 760.0, 780.0,
800.0, 820.0, 840.0, 860.0, 880.0, 900.0, 920.0, 940.0, 960.0, 980.0,
1000.0, 1020.0, 1040.0, 1060.0, 1080.0, 1100.0, 1120.0, 1140.0, 1160.0, 1180.0,
1200.0, 1220.0, 1240.0, 1260.0, 1280.0, 1300.0, 1320.0, 1340.0, 1360.0, 1380.0,
1400.0, 1420.0, 1440.0, 1460.0, 1480.0, 1500.0, 1520.0, 1540.0, 1560.0, 1580.0,
1600.0, 1620.0, 1640.0, 1660.0, 1680.0, 1700.0, 1720.0, 1740.0, 1760.0, 1780.0,
1800.0, 1820.0, 1840.0, 1860.0, 1880.0, 1900.0, 1920.0, 1940.0, 1960.0, 1980.0,
2000.0, 2020.0, 2040.0, 2060.0, 2080.0, 2100.0, 2120.0, 2140.0, 2160.0, 2180.0,
2200.0, 2220.0, 2240.0, 2260.0, 2280.0, 2300.0, 2320.0, 2340.0, 2360.0, 2380.0,
2400.0, 2420.0, 2440.0, 2460.0, 2480.0, 2500.0, 2520.0, 2540.0, 2560.0, 2580.0,
2600.0, 2620.0, 2640.0, 2660.0, 2680.0, 2700.0, 2720.0, 2740.0, 2760.0, 2780.0,
2800.0, 2820.0, 2840.0, 2860.0, 2880.0, 2900.0, 2920.0, 2940.0, 2960.0, 2980.0,
3000.0, 3020.0, 3040.0, 3060.0, 3080.0, 3100.0, 3120.0, 3140.0, 3160.0, 3180.0,
3200.0, 3220.0, 3240.0, 3260.0, 3280.0, 3300.0, 3320.0, 3340.0, 3360.0, 3380.0,
3400.0, 3420.0, 3440.0, 3460.0, 3480.0, 3500.0,
])
TIPS_2017_ISOT[3] = float64([
1.0, 20.0, 40.0, 60.0, 80.0, 100.0, 120.0, 140.0, 160.0, 180.0,
200.0, 220.0, 240.0, 260.0, 280.0, 300.0, 320.0, 340.0, 360.0, 380.0,
400.0, 420.0, 440.0, 460.0, 480.0, 500.0, 520.0, 540.0, 560.0, 580.0,
600.0, 620.0, 640.0, 660.0, 680.0, 700.0, 720.0, 740.0, 760.0, 780.0,
800.0, 820.0, 840.0, 860.0, 880.0, 900.0, 920.0, 940.0, 960.0, 980.0,
1000.0, 1020.0, 1040.0, 1060.0, 1080.0, 1100.0, 1120.0, 1140.0, 1160.0, 1180.0,
1200.0, 1220.0, 1240.0, 1260.0, 1280.0, 1300.0, 1320.0, 1340.0, 1360.0, 1380.0,
1400.0, 1420.0, 1440.0, 1460.0, 1480.0, 1500.0, 1520.0, 1540.0, 1560.0, 1580.0,
1600.0, 1620.0, 1640.0, 1660.0, 1680.0, 1700.0, 1720.0, 1740.0, 1760.0, 1780.0,
1800.0, 1820.0, 1840.0, 1860.0, 1880.0, 1900.0, 1920.0, 1940.0, 1960.0, 1980.0,
2000.0, 2020.0, 2040.0, 2060.0, 2080.0, 2100.0, 2120.0, 2140.0, 2160.0, 2180.0,
2200.0, 2220.0, 2240.0, 2260.0, 2280.0, 2300.0, 2320.0, 2340.0, 2360.0, 2380.0,
2400.0, 2420.0, 2440.0, 2460.0, 2480.0, 2500.0, 2520.0, 2540.0, 2560.0, 2580.0,
2600.0, 2620.0, 2640.0, 2660.0, 2680.0, 2700.0, 2720.0, 2740.0, 2760.0, 2780.0,
2800.0, 2820.0, 2840.0, 2860.0, 2880.0, 2900.0, 2920.0, 2940.0, 2960.0, 2980.0,
3000.0, 3020.0, 3040.0, 3060.0, 3080.0, 3100.0, 3120.0, 3140.0, 3160.0, 3180.0,
3200.0, 3220.0, 3240.0, 3260.0, 3280.0, 3300.0, 3320.0, 3340.0, 3360.0, 3380.0,
3400.0, 3420.0, 3440.0, 3460.0, 3480.0, 3500.0, 3520.0, 3540.0, 3560.0, 3580.0,
3600.0, 3620.0, 3640.0, 3660.0, 3680.0, 3700.0, 3720.0, 3740.0, 3760.0, 3780.0,
3800.0, 3820.0, 3840.0, 3860.0, 3880.0, 3900.0, 3920.0, 3940.0, 3960.0, 3980.0,
4000.0, 4020.0, 4040.0, 4060.0, 4080.0, 4100.0, 4120.0, 4140.0, 4160.0, 4180.0,
4200.0, 4220.0, 4240.0, 4260.0, 4280.0, 4300.0, 4320.0, 4340.0, 4360.0, 4380.0,
4400.0, 4420.0, 4440.0, 4460.0, 4480.0, 4500.0, 4520.0, 4540.0, 4560.0, 4580.0,
4600.0, 4620.0, 4640.0, 4660.0, 4680.0, 4700.0, 4720.0, 4740.0, 4760.0, 4780.0,
4800.0, 4820.0, 4840.0, 4860.0, 4880.0, 4900.0, 4920.0, 4940.0, 4960.0, 4980.0,
5000.0, 5020.0, 5040.0, 5060.0, 5080.0, 5100.0, 5120.0, 5140.0, 5160.0, 5180.0,
5200.0, 5220.0, 5240.0, 5260.0, 5280.0, 5300.0, 5320.0, 5340.0, 5360.0, 5380.0,
5400.0, 5420.0, 5440.0, 5460.0, 5480.0, 5500.0, 5520.0, 5540.0, 5560.0, 5580.0,
5600.0, 5620.0, 5640.0, 5660.0, 5680.0, 5700.0, 5720.0, 5740.0, 5760.0, 5780.0,
5800.0, 5820.0, 5840.0, 5860.0, 5880.0, 5900.0, 5920.0, 5940.0, 5960.0, 5980.0,
6000.0, 6020.0, 6040.0, 6060.0, 6080.0, 6100.0, 6120.0, 6140.0, 6160.0, 6180.0,
6200.0, 6220.0, 6240.0, 6260.0, 6280.0, 6300.0, 6320.0, 6340.0, 6360.0, 6380.0,
6400.0, 6420.0, 6440.0, 6460.0, 6480.0, 6500.0, 6520.0, 6540.0, 6560.0, 6580.0,
6600.0, 6620.0, 6640.0, 6660.0, 6680.0, 6700.0, 6720.0, 6740.0, 6760.0, 6780.0,
6800.0, 6820.0, 6840.0, 6860.0, 6880.0, 6900.0, 6920.0, 6940.0, 6960.0, 6980.0,
7000.0, 7020.0, 7040.0, 7060.0, 7080.0, 7100.0, 7120.0, 7140.0, 7160.0, 7180.0,
7200.0, 7220.0, 7240.0, 7260.0, 7280.0, 7300.0, 7320.0, 7340.0, 7360.0, 7380.0,
7400.0, 7420.0, 7440.0, 7460.0, 7480.0, 7500.0, 7520.0, 7540.0, 7560.0, 7580.0,
7600.0, 7620.0, 7640.0, 7660.0, 7680.0, 7700.0, 7720.0, 7740.0, 7760.0, 7780.0,
7800.0, 7820.0, 7840.0, 7860.0, 7880.0, 7900.0, 7920.0, 7940.0, 7960.0, 7980.0,
8000.0, 8020.0, 8040.0, 8060.0, 8080.0, 8100.0, 8120.0, 8140.0, 8160.0, 8180.0,
8200.0, 8220.0, 8240.0, 8260.0, 8280.0, 8300.0, 8320.0, 8340.0, 8360.0, 8380.0,
8400.0, 8420.0, 8440.0, 8460.0, 8480.0, 8500.0, 8520.0, 8540.0, 8560.0, 8580.0,
8600.0, 8620.0, 8640.0, 8660.0, 8680.0, 8700.0, 8720.0, 8740.0, 8760.0, 8780.0,
8800.0, 8820.0, 8840.0, 8860.0, 8880.0, 8900.0, 8920.0, 8940.0, 8960.0, 8980.0,
9000.0,
])
TIPS_2017_ISOT[4] = float64([
1.0, 20.0, 40.0, 60.0, 80.0, 100.0, 120.0, 140.0, 160.0, 180.0,
200.0, 220.0, 240.0, 260.0, 280.0, 300.0, 320.0, 340.0, 360.0, 380.0,
400.0, 420.0, 440.0, 460.0, 480.0, 500.0, 520.0, 540.0, 560.0, 580.0,
600.0, 620.0, 640.0, 660.0, 680.0, 700.0, 720.0, 740.0, 760.0, 780.0,
800.0, 820.0, 840.0, 860.0, 880.0, 900.0, 920.0, 940.0, 960.0, 980.0,
1000.0, 1020.0, 1040.0, 1060.0, 1080.0, 1100.0, 1120.0, 1140.0, 1160.0, 1180.0,
1200.0, 1220.0, 1240.0, 1260.0, 1280.0, 1300.0, 1320.0, 1340.0, 1360.0, 1380.0,
1400.0, 1420.0, 1440.0, 1460.0, 1480.0, 1500.0, 1520.0, 1540.0, 1560.0, 1580.0,
1600.0, 1620.0, 1640.0, 1660.0, 1680.0, 1700.0, 1720.0, 1740.0, 1760.0, 1780.0,
1800.0, 1820.0, 1840.0, 1860.0, 1880.0, 1900.0, 1920.0, 1940.0, 1960.0, 1980.0,
2000.0, 2020.0, 2040.0, 2060.0, 2080.0, 2100.0, 2120.0, 2140.0, 2160.0, 2180.0,
2200.0, 2220.0, 2240.0, 2260.0, 2280.0, 2300.0, 2320.0, 2340.0, 2360.0, 2380.0,
2400.0, 2420.0, 2440.0, 2460.0, 2480.0, 2500.0, 2520.0, 2540.0, 2560.0, 2580.0,
2600.0, 2620.0, 2640.0, 2660.0, 2680.0, 2700.0, 2720.0, 2740.0, 2760.0, 2780.0,
2800.0, 2820.0, 2840.0, 2860.0, 2880.0, 2900.0, 2920.0, 2940.0, 2960.0, 2980.0,
3000.0, 3020.0, 3040.0, 3060.0, 3080.0, 3100.0, 3120.0, 3140.0, 3160.0, 3180.0,
3200.0, 3220.0, 3240.0, 3260.0, 3280.0, 3300.0, 3320.0, 3340.0, 3360.0, 3380.0,
3400.0, 3420.0, 3440.0, 3460.0, 3480.0, 3500.0, 3520.0, 3540.0, 3560.0, 3580.0,
3600.0, 3620.0, 3640.0, 3660.0, 3680.0, 3700.0, 3720.0, 3740.0, 3760.0, 3780.0,
3800.0, 3820.0, 3840.0, 3860.0, 3880.0, 3900.0, 3920.0, 3940.0, 3960.0, 3980.0,
4000.0, 4020.0, 4040.0, 4060.0, 4080.0, 4100.0, 4120.0, 4140.0, 4160.0, 4180.0,
4200.0, 4220.0, 4240.0, 4260.0, 4280.0, 4300.0, 4320.0, 4340.0, 4360.0, 4380.0,
4400.0, 4420.0, 4440.0, 4460.0, 4480.0, 4500.0,
])
TIPS_2017_ISOT[5] = float64([
1.0, 20.0, 40.0, 60.0, 80.0, 100.0, 120.0, 140.0, 160.0, 180.0,
200.0, 220.0, 240.0, 260.0, 280.0, 300.0, 320.0, 340.0, 360.0, 380.0,
400.0, 420.0, 440.0, 460.0, 480.0, 500.0, 520.0, 540.0, 560.0, 580.0,
600.0, 620.0, 640.0, 660.0, 680.0, 700.0, 720.0, 740.0, 760.0, 780.0,
800.0, 820.0, 840.0, 860.0, 880.0, 900.0, 920.0, 940.0, 960.0, 980.0,
1000.0, 1020.0, 1040.0, 1060.0, 1080.0, 1100.0, 1120.0, 1140.0, 1160.0, 1180.0,
1200.0, 1220.0, 1240.0, 1260.0, 1280.0, 1300.0, 1320.0, 1340.0, 1360.0, 1380.0,
1400.0, 1420.0, 1440.0, 1460.0, 1480.0, 1500.0, 1520.0, 1540.0, 1560.0, 1580.0,
1600.0, 1620.0, 1640.0, 1660.0, 1680.0, 1700.0, 1720.0, 1740.0, 1760.0, 1780.0,
1800.0, 1820.0, 1840.0, 1860.0, 1880.0, 1900.0, 1920.0, 1940.0, 1960.0, 1980.0,
2000.0, 2020.0, 2040.0, 2060.0, 2080.0, 2100.0, 2120.0, 2140.0, 2160.0, 2180.0,
2200.0, 2220.0, 2240.0, 2260.0, 2280.0, 2300.0, 2320.0, 2340.0, 2360.0, 2380.0,
2400.0, 2420.0, 2440.0, 2460.0, 2480.0, 2500.0, 2520.0, 2540.0, 2560.0, 2580.0,
2600.0, 2620.0, 2640.0, 2660.0, 2680.0, 2700.0, 2720.0, 2740.0, 2760.0, 2780.0,
2800.0, 2820.0, 2840.0, 2860.0, 2880.0, 2900.0, 2920.0, 2940.0, 2960.0, 2980.0,
3000.0, 3020.0, 3040.0, 3060.0, 3080.0, 3100.0, 3120.0, 3140.0, 3160.0, 3180.0,
3200.0, 3220.0, 3240.0, 3260.0, 3280.0, 3300.0, 3320.0, 3340.0, 3360.0, 3380.0,
3400.0, 3420.0, 3440.0, 3460.0, 3480.0, 3500.0, 3520.0, 3540.0, 3560.0, 3580.0,
3600.0, 3620.0, 3640.0, 3660.0, 3680.0, 3700.0, 3720.0, 3740.0, 3760.0, 3780.0,
3800.0, 3820.0, 3840.0, 3860.0, 3880.0, 3900.0, 3920.0, 3940.0, 3960.0, 3980.0,
4000.0, 4020.0, 4040.0, 4060.0, 4080.0, 4100.0, 4120.0, 4140.0, 4160.0, 4180.0,
4200.0, 4220.0, 4240.0, 4260.0, 4280.0, 4300.0, 4320.0, 4340.0, 4360.0, 4380.0,
4400.0, 4420.0, 4440.0, 4460.0, 4480.0, 4500.0, 4520.0, 4540.0, 4560.0, 4580.0,
4600.0, 4620.0, 4640.0, 4660.0, 4680.0, 4700.0, 4720.0, 4740.0, 4760.0, 4780.0,
4800.0, 4820.0, 4840.0, 4860.0, 4880.0, 4900.0, 4920.0, 4940.0, 4960.0, 4980.0,
5000.0, 5020.0, 5040.0, 5060.0, 5080.0, 5100.0, 5120.0, 5140.0, 5160.0, 5180.0,
5200.0, 5220.0, 5240.0, 5260.0, 5280.0, 5300.0, 5320.0, 5340.0, 5360.0, 5380.0,
5400.0, 5420.0, 5440.0, 5460.0, 5480.0, 5500.0, 5520.0, 5540.0, 5560.0, 5580.0,
5600.0, 5620.0, 5640.0, 5660.0, 5680.0, 5700.0, 5720.0, 5740.0, 5760.0, 5780.0,
5800.0, 5820.0, 5840.0, 5860.0, 5880.0, 5900.0, 5920.0, 5940.0, 5960.0, 5980.0,
6000.0, 6020.0, 6040.0, 6060.0, 6080.0, 6100.0, 6120.0, 6140.0, 6160.0, 6180.0,
6200.0, 6220.0, 6240.0, 6260.0, 6280.0, 6300.0, 6320.0, 6340.0, 6360.0, 6380.0,
6400.0, 6420.0, 6440.0, 6460.0, 6480.0, 6500.0, 6520.0, 6540.0, 6560.0, 6580.0,
6600.0, 6620.0, 6640.0, 6660.0, 6680.0, 6700.0, 6720.0, 6740.0, 6760.0, 6780.0,
6800.0, 6820.0, 6840.0, 6860.0, 6880.0, 6900.0, 6920.0, 6940.0, 6960.0, 6980.0,
7000.0, 7020.0, 7040.0, 7060.0, 7080.0, 7100.0, 7120.0, 7140.0, 7160.0, 7180.0,
7200.0, 7220.0, 7240.0, 7260.0, 7280.0, 7300.0, 7320.0, 7340.0, 7360.0, 7380.0,
7400.0, 7420.0, 7440.0, 7460.0, 7480.0, 7500.0,
])
TIPS_2017_ISOT[6] = float64([
1.0, 20.0, 40.0, 60.0, 80.0, 100.0, 120.0, 140.0, 160.0, 180.0,
200.0, 220.0, 240.0, 260.0, 280.0, 300.0, 320.0, 340.0, 360.0, 380.0,
400.0, 420.0, 440.0, 460.0, 480.0, 500.0, 520.0, 540.0, 560.0, 580.0,
600.0, 620.0, 640.0, 660.0, 680.0, 700.0, 720.0, 740.0, 760.0, 780.0,
800.0, 820.0, 840.0, 860.0, 880.0, 900.0, 920.0, 940.0, 960.0, 980.0,
1000.0, 1020.0, 1040.0, 1060.0, 1080.0, 1100.0, 1120.0, 1140.0, 1160.0, 1180.0,
1200.0, 1220.0, 1240.0, 1260.0, 1280.0, 1300.0, 1320.0, 1340.0, 1360.0, 1380.0,
1400.0, 1420.0, 1440.0, 1460.0, 1480.0, 1500.0, 1520.0, 1540.0, 1560.0, 1580.0,
1600.0, 1620.0, 1640.0, 1660.0, 1680.0, 1700.0, 1720.0, 1740.0, 1760.0, 1780.0,
1800.0, 1820.0, 1840.0, 1860.0, 1880.0, 1900.0, 1920.0, 1940.0, 1960.0, 1980.0,
2000.0, 2020.0, 2040.0, 2060.0, 2080.0, 2100.0, 2120.0, 2140.0, 2160.0, 2180.0,
2200.0, 2220.0, 2240.0, 2260.0, 2280.0, 2300.0, 2320.0, 2340.0, 2360.0, 2380.0,
2400.0, 2420.0, 2440.0, 2460.0, 2480.0, 2500.0, 2520.0, 2540.0, 2560.0, 2580.0,
2600.0, 2620.0, 2640.0, 2660.0, 2680.0, 2700.0, 2720.0, 2740.0, 2760.0, 2780.0,
2800.0, 2820.0, 2840.0, 2860.0, 2880.0, 2900.0, 2920.0, 2940.0, 2960.0, 2980.0,
3000.0, 3020.0, 3040.0, 3060.0, 3080.0, 3100.0, 3120.0, 3140.0, 3160.0, 3180.0,
3200.0, 3220.0, 3240.0, 3260.0, 3280.0, 3300.0, 3320.0, 3340.0, 3360.0, 3380.0,
3400.0, 3420.0, 3440.0, 3460.0, 3480.0, 3500.0, 3520.0, 3540.0, 3560.0, 3580.0,
3600.0, 3620.0, 3640.0, 3660.0, 3680.0, 3700.0, 3720.0, 3740.0, 3760.0, 3780.0,
3800.0, 3820.0, 3840.0, 3860.0, 3880.0, 3900.0, 3920.0, 3940.0, 3960.0, 3980.0,
4000.0,
])
TIPS_2017_ISOT_HASH = {}
TIPS_2017_ISOQ_HASH = {}
# ---------------------- M = 1, I = 1 ---------------------------
M = 1
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[0]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.000000E+00, 3.348920E+00, 9.416960E+00, 1.681863E+01, 2.543182E+01, 3.515313E+01,
4.587953E+01, 5.752827E+01, 7.003401E+01, 8.334408E+01, 9.741515E+01, 1.122112E+02,
1.277022E+02, 1.438633E+02, 1.606747E+02, 1.781206E+02, 1.961892E+02, 2.148721E+02,
2.341643E+02, 2.540633E+02, 2.745691E+02, 2.956837E+02, 3.174110E+02, 3.397563E+02,
3.627261E+02, 3.863281E+02, 4.105710E+02, 4.354642E+02, 4.610180E+02, 4.872432E+02,
5.141514E+02, 5.417546E+02, 5.700656E+02, 5.990973E+02, 6.288636E+02, 6.593787E+02,
6.906572E+02, 7.227143E+02, 7.555656E+02, 7.892274E+02, 8.237162E+02, 8.590491E+02,
8.952437E+02, 9.323179E+02, 9.702901E+02, 1.009179E+03, 1.049005E+03, 1.089786E+03,
1.131543E+03, 1.174297E+03, 1.218069E+03, 1.262880E+03, 1.308752E+03, 1.355706E+03,
1.403766E+03, 1.452954E+03, 1.503294E+03, 1.554809E+03, 1.607523E+03, 1.661461E+03,
1.716647E+03, 1.773106E+03, 1.830864E+03, 1.889947E+03, 1.950381E+03, 2.012191E+03,
2.075405E+03, 2.140051E+03, 2.206155E+03, 2.273745E+03, 2.342850E+03, 2.413498E+03,
2.485717E+03, 2.559538E+03, 2.634989E+03, 2.712101E+03, 2.790904E+03, 2.871427E+03,
2.953704E+03, 3.037763E+03, 3.123638E+03, 3.211360E+03, 3.300961E+03, 3.392475E+03,
3.485933E+03, 3.581371E+03, 3.678821E+03, 3.778318E+03, 3.879895E+03, 3.983589E+03,
4.089435E+03, 4.197467E+03, 4.307722E+03, 4.420237E+03, 4.535049E+03, 4.652193E+03,
4.771709E+03, 4.893634E+03, 5.018006E+03, 5.144865E+03, 5.274249E+03, 5.406197E+03,
5.540751E+03, 5.677950E+03, 5.817835E+03, 5.960448E+03, 6.105829E+03, 6.254022E+03,
6.405068E+03, 6.559010E+03, 6.715891E+03, 6.875757E+03, 7.038649E+03, 7.204614E+03,
7.373696E+03, 7.545940E+03, 7.721393E+03, 7.900101E+03, 8.082111E+03, 8.267469E+03,
8.456223E+03, 8.648422E+03, 8.844114E+03, 9.043348E+03, 9.246173E+03, 9.452640E+03,
9.662798E+03, 9.876698E+03, 1.009439E+04, 1.031593E+04, 1.054137E+04, 1.077075E+04,
1.100414E+04, 1.124158E+04, 1.148314E+04, 1.172886E+04, 1.197879E+04, 1.223300E+04,
1.249154E+04, 1.275447E+04, 1.302184E+04, 1.329370E+04, 1.357012E+04, 1.385116E+04,
1.413687E+04, 1.442730E+04, 1.472253E+04, 1.502260E+04, 1.532759E+04, 1.563754E+04,
1.595251E+04, 1.627258E+04, 1.659780E+04, 1.692823E+04, 1.726394E+04, 1.760498E+04,
1.795142E+04, 1.830332E+04, 1.866075E+04, 1.902377E+04, 1.939243E+04, 1.976682E+04,
2.014699E+04, 2.053301E+04, 2.092493E+04, 2.132284E+04, 2.172678E+04, 2.213684E+04,
2.255307E+04, 2.297555E+04, 2.340433E+04, 2.383949E+04, 2.428109E+04, 2.472920E+04,
2.518388E+04, 2.564522E+04, 2.611326E+04, 2.658809E+04, 2.706977E+04, 2.755836E+04,
2.805395E+04, 2.855658E+04, 2.906634E+04, 2.958330E+04, 3.010751E+04, 3.063906E+04,
3.117801E+04, 3.172442E+04, 3.227838E+04, 3.283994E+04, 3.340917E+04, 3.398616E+04,
3.457096E+04, 3.516364E+04, 3.576428E+04, 3.637294E+04, 3.698969E+04, 3.761461E+04,
3.824775E+04, 3.888919E+04, 3.953900E+04, 4.019725E+04, 4.086401E+04, 4.153933E+04,
4.222330E+04, 4.291598E+04, 4.361744E+04, 4.432774E+04, 4.504696E+04, 4.577516E+04,
4.651241E+04, 4.725878E+04, 4.801433E+04, 4.877912E+04, 4.955323E+04, 5.033673E+04,
5.112967E+04, 5.193213E+04, 5.274416E+04, 5.356584E+04, 5.439722E+04, 5.523838E+04,
5.608937E+04, 5.695027E+04, 5.782113E+04, 5.870201E+04, 5.959298E+04, 6.049411E+04,
6.140545E+04, 6.232706E+04, 6.325900E+04, 6.420135E+04, 6.515415E+04, 6.611746E+04,
6.709136E+04, 6.807588E+04, 6.907110E+04, 7.007707E+04, 7.109385E+04, 7.212149E+04,
7.316006E+04, 7.420960E+04, 7.527018E+04, 7.634185E+04, 7.742466E+04, 7.851867E+04,
7.962393E+04, 8.074050E+04, 8.186842E+04, 8.300775E+04, 8.415854E+04,
])
# ---------------------- M = 1, I = 2 ---------------------------
M = 1
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[0]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.000000E+00, 3.372790E+00, 9.492280E+00, 1.695557E+01, 2.564104E+01, 3.544414E+01,
4.626091E+01, 5.800788E+01, 7.061916E+01, 8.404163E+01, 9.823160E+01, 1.131528E+02,
1.287749E+02, 1.450731E+02, 1.620274E+02, 1.796219E+02, 1.978450E+02, 2.166883E+02,
2.361468E+02, 2.562183E+02, 2.769027E+02, 2.982024E+02, 3.201213E+02, 3.426649E+02,
3.658398E+02, 3.896541E+02, 4.141165E+02, 4.392366E+02, 4.650249E+02, 4.914924E+02,
5.186508E+02, 5.465124E+02, 5.750899E+02, 6.043966E+02, 6.344463E+02, 6.652532E+02,
6.968322E+02, 7.291983E+02, 7.623672E+02, 7.963551E+02, 8.311783E+02, 8.668538E+02,
9.033990E+02, 9.462183E+02, 2.769027E+02, 2.982024E+02, 3.201213E+02, 3.426649E+02,
3.658398E+02, 3.896541E+02, 4.141165E+02, 4.392366E+02, 4.650249E+02, 4.914924E+02,
5.186508E+02, 5.465124E+02, 5.750899E+02, 6.043966E+02, 6.344463E+02, 6.652532E+02,
6.968322E+02, 7.291983E+02, 7.623672E+02, 7.963551E+02, 8.311783E+02, 8.668538E+02,
9.033990E+02, 9.408315E+02, 9.791695E+02, 1.018432E+03, 1.058637E+03, 1.099804E+03,
1.141952E+03, 1.185103E+03, 1.229276E+03, 1.274491E+03, 1.320770E+03, 1.368133E+03,
1.416603E+03, 1.466202E+03, 1.516951E+03, 1.568873E+03, 1.621992E+03, 1.676329E+03,
1.731908E+03, 1.788753E+03, 1.846888E+03, 1.906337E+03, 1.967124E+03, 2.029275E+03,
2.092814E+03, 2.157766E+03, 2.224157E+03, 2.292012E+03, 2.361359E+03, 2.432222E+03,
2.504629E+03, 2.578606E+03, 2.654181E+03, 2.731380E+03, 2.810232E+03, 2.890764E+03,
2.973005E+03, 3.056982E+03, 3.142725E+03, 3.230262E+03, 3.319623E+03, 3.410837E+03,
3.503933E+03, 3.598943E+03, 3.695895E+03, 3.794822E+03, 3.895752E+03, 3.998719E+03,
4.103752E+03, 4.210885E+03, 4.320147E+03, 4.431573E+03, 4.545194E+03, 4.661044E+03,
4.779155E+03, 4.899561E+03, 5.022295E+03, 5.147392E+03, 5.274886E+03, 5.404811E+03,
5.537202E+03, 5.672095E+03, 5.809525E+03, 5.949527E+03, 6.092139E+03, 6.237395E+03,
6.385334E+03, 6.535992E+03, 6.689406E+03, 6.845614E+03, 7.004655E+03, 7.166565E+03,
7.331385E+03, 7.499153E+03, 7.669907E+03, 7.843689E+03, 8.020537E+03, 8.200491E+03,
8.383594E+03, 8.569884E+03, 8.759404E+03, 8.952195E+03, 9.148299E+03, 9.347757E+03,
9.550614E+03, 9.756911E+03, 9.966691E+03, 1.018000E+04, 1.039688E+04, 1.061737E+04,
1.084152E+04, 1.106938E+04, 1.130099E+04, 1.153639E+04, 1.177564E+04, 1.201877E+04,
1.226583E+04, 1.251688E+04, 1.277195E+04, 1.303110E+04, 1.329438E+04, 1.356182E+04,
1.383348E+04, 1.410942E+04, 1.438967E+04, 1.467429E+04, 1.496333E+04, 1.525683E+04,
1.555486E+04, 1.585745E+04, 1.616467E+04, 1.647655E+04, 1.679316E+04, 1.711455E+04,
1.744077E+04, 1.777186E+04, 1.810790E+04, 1.844892E+04, 1.879498E+04, 1.914614E+04,
1.950245E+04, 1.986396E+04, 2.023074E+04, 2.060283E+04, 2.098029E+04, 2.136318E+04,
2.175156E+04, 2.214547E+04, 2.254498E+04, 2.295015E+04, 2.336103E+04, 2.377768E+04,
2.420015E+04, 2.462851E+04, 2.506282E+04, 2.550313E+04, 2.594950E+04, 2.640200E+04,
2.686067E+04, 2.732559E+04, 2.779681E+04, 2.827439E+04, 2.875840E+04, 2.924889E+04,
2.974592E+04, 3.024956E+04, 3.075987E+04, 3.127691E+04, 3.180074E+04, 3.233143E+04,
3.286903E+04, 3.341361E+04, 3.396524E+04, 3.452397E+04, 3.508988E+04, 3.566301E+04,
3.624345E+04, 3.683124E+04, 3.742646E+04, 3.802917E+04, 3.863943E+04, 3.925731E+04,
3.988288E+04, 4.051619E+04, 4.115732E+04, 4.180633E+04, 4.246328E+04, 4.312824E+04,
4.380128E+04, 4.448245E+04, 4.517184E+04, 4.586950E+04, 4.657550E+04, 4.728991E+04,
4.801279E+04, 4.874421E+04, 4.948423E+04, 5.023293E+04, 5.099037E+04, 5.175661E+04,
5.253172E+04, 5.331578E+04, 5.410884E+04, 5.491098E+04, 5.572226E+04, 5.654275E+04,
5.737251E+04, 5.821161E+04, 5.906013E+04, 5.991812E+04, 6.078566E+04, 6.166281E+04,
6.254964E+04, 6.344622E+04, 6.435261E+04, 6.526889E+04, 6.619511E+04, 6.713135E+04,
6.807767E+04, 6.903414E+04, 7.000083E+04, 7.097781E+04, 7.196513E+04, 7.296288E+04,
7.397111E+04, 7.498990E+04, 7.601930E+04, 7.705940E+04, 7.811024E+04,
])
# ---------------------- M = 1, I = 3 ---------------------------
M = 1
I = 3
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[0]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
6.000000E+00, 2.016901E+01, 5.674024E+01, 1.013453E+02, 1.532533E+02, 2.118401E+02,
2.764846E+02, 3.466880E+02, 4.220566E+02, 5.022729E+02, 5.870757E+02, 6.762480E+02,
7.696091E+02, 8.670100E+02, 9.683305E+02, 1.073476E+03, 1.182375E+03, 1.294979E+03,
1.411255E+03, 1.531189E+03, 1.654781E+03, 1.782042E+03, 1.912994E+03, 2.047669E+03,
2.186104E+03, 2.328346E+03, 2.474446E+03, 2.624459E+03, 2.778445E+03, 2.936469E+03,
3.098600E+03, 3.264908E+03, 3.435470E+03, 3.610362E+03, 3.789666E+03, 3.973467E+03,
4.161852E+03, 4.354909E+03, 4.552733E+03, 4.755418E+03, 4.963062E+03, 5.175767E+03,
5.393635E+03, 5.616772E+03, 5.845286E+03, 6.079288E+03, 6.318891E+03, 6.564209E+03,
6.815361E+03, 7.072464E+03, 7.335642E+03, 7.605018E+03, 7.880717E+03, 8.162866E+03,
8.451596E+03, 8.747038E+03, 9.049324E+03, 9.358591E+03, 9.674973E+03, 9.998611E+03,
1.032964E+04, 1.066821E+04, 1.101447E+04, 1.136854E+04, 1.173059E+04, 1.210077E+04,
1.247921E+04, 1.286608E+04, 1.326152E+04, 1.366570E+04, 1.407876E+04, 1.450087E+04,
1.493219E+04, 1.537288E+04, 1.582310E+04, 1.628301E+04, 1.675279E+04, 1.723259E+04,
1.772260E+04, 1.822297E+04, 1.873389E+04, 1.925553E+04, 1.978806E+04, 2.033166E+04,
2.088651E+04, 2.145280E+04, 2.203069E+04, 2.262039E+04, 2.322207E+04, 2.383593E+04,
2.446215E+04, 2.510092E+04, 2.575244E+04, 2.641690E+04, 2.709449E+04, 2.778543E+04,
2.848989E+04, 2.920810E+04, 2.994025E+04, 3.068655E+04, 3.144720E+04, 3.222241E+04,
3.301240E+04, 3.381738E+04, 3.463755E+04, 3.547315E+04, 3.632438E+04, 3.719147E+04,
3.807464E+04, 3.897411E+04, 3.989012E+04, 4.082287E+04, 4.177262E+04, 4.273959E+04,
4.372400E+04, 4.472611E+04, 4.574614E+04, 4.678433E+04, 4.784094E+04, 4.891619E+04,
5.001034E+04, 5.112363E+04, 5.225631E+04, 5.340864E+04, 5.458086E+04, 5.577324E+04,
5.698602E+04, 5.821948E+04, 5.947386E+04, 6.074944E+04, 6.204648E+04, 6.336525E+04,
6.470602E+04, 6.606905E+04, 6.745462E+04, 6.886301E+04, 7.029450E+04, 7.174936E+04,
7.322787E+04, 7.473032E+04, 7.625700E+04, 7.780820E+04, 7.938419E+04, 8.098528E+04,
8.261176E+04, 8.426392E+04, 8.594206E+04, 8.764648E+04, 8.937748E+04, 9.113537E+04,
9.292045E+04, 9.473303E+04, 9.657342E+04, 9.844193E+04, 1.003389E+05, 1.022646E+05,
1.042193E+05, 1.062035E+05, 1.082173E+05, 1.102612E+05, 1.123354E+05, 1.144403E+05,
1.165763E+05, 1.187435E+05, 1.209425E+05, 1.231734E+05, 1.254367E+05, 1.277326E+05,
1.300616E+05, 1.324240E+05, 1.348200E+05, 1.372501E+05, 1.397146E+05, 1.422138E+05,
1.447482E+05, 1.473179E+05, 1.499235E+05, 1.525652E+05, 1.552435E+05, 1.579586E+05,
1.607109E+05, 1.635008E+05, 1.663287E+05, 1.691948E+05, 1.720997E+05, 1.750436E+05,
1.780269E+05, 1.810500E+05, 1.841132E+05, 1.872170E+05, 1.903617E+05, 1.935477E+05,
1.967753E+05, 2.000449E+05, 2.033570E+05, 2.067118E+05, 2.101098E+05, 2.135514E+05,
2.170369E+05, 2.205667E+05, 2.241412E+05, 2.277608E+05, 2.314260E+05, 2.351369E+05,
2.388942E+05, 2.426981E+05, 2.465490E+05, 2.504474E+05, 2.543936E+05, 2.583880E+05,
2.624310E+05, 2.665231E+05, 2.706645E+05, 2.748558E+05, 2.790973E+05, 2.833893E+05,
2.877324E+05, 2.921269E+05, 2.965732E+05, 3.010717E+05, 3.056228E+05, 3.102268E+05,
3.148843E+05, 3.195956E+05, 3.243612E+05, 3.291813E+05, 3.340565E+05, 3.389870E+05,
3.439734E+05, 3.490161E+05, 3.541153E+05, 3.592716E+05, 3.644854E+05, 3.697570E+05,
3.750868E+05, 3.804753E+05, 3.859229E+05, 3.914300E+05, 3.969969E+05, 4.026240E+05,
4.083119E+05, 4.140609E+05, 4.198713E+05, 4.257436E+05, 4.316783E+05, 4.376756E+05,
4.437360E+05, 4.498599E+05, 4.560478E+05, 4.622999E+05, 4.686168E+05,
])
# ---------------------- M = 1, I = 4 ---------------------------
M = 1
I = 4
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[0]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
6.000000E+00, 1.766833E+01, 4.570241E+01, 8.177074E+01, 1.242917E+02, 1.724141E+02,
2.255614E+02, 2.833095E+02, 3.453297E+02, 4.113595E+02, 4.811860E+02, 5.546366E+02,
6.315735E+02, 7.118902E+02, 7.955081E+02, 8.823735E+02, 9.724548E+02, 1.065740E+03,
1.162232E+03, 1.261951E+03, 1.364927E+03, 1.471199E+03, 1.580818E+03, 1.693839E+03,
1.810326E+03, 1.930347E+03, 2.053977E+03, 2.181293E+03, 2.312379E+03, 2.447320E+03,
2.586208E+03, 2.729136E+03, 2.876201E+03, 3.027504E+03, 3.183148E+03, 3.343240E+03,
3.507890E+03, 3.677211E+03, 3.851318E+03, 4.030329E+03, 4.214365E+03, 4.403551E+03,
4.598012E+03, 4.797878E+03, 5.003279E+03, 5.214351E+03, 5.431230E+03, 5.654054E+03,
5.882966E+03, 6.118108E+03, 6.359628E+03, 6.607674E+03, 6.862397E+03, 7.123950E+03,
7.392489E+03, 7.668172E+03, 7.951159E+03, 8.241613E+03, 8.539699E+03, 8.845584E+03,
9.159437E+03, 9.481431E+03, 9.811739E+03, 1.015054E+04, 1.049801E+04, 1.085433E+04,
1.121969E+04, 1.159426E+04, 1.197825E+04, 1.237183E+04, 1.277521E+04, 1.318858E+04,
1.361213E+04, 1.404607E+04, 1.449060E+04, 1.494593E+04, 1.541225E+04, 1.588979E+04,
1.637875E+04, 1.687936E+04, 1.739182E+04, 1.791635E+04, 1.845319E+04, 1.900254E+04,
1.956465E+04, 2.013974E+04, 2.072804E+04, 2.132979E+04, 2.194522E+04, 2.257457E+04,
2.321808E+04, 2.387600E+04, 2.454858E+04, 2.523606E+04, 2.593868E+04, 2.665672E+04,
2.739041E+04, 2.814002E+04, 2.890581E+04, 2.968803E+04, 3.048695E+04, 3.130284E+04,
3.213596E+04, 3.298659E+04, 3.385498E+04, 3.474142E+04, 3.564619E+04, 3.656955E+04,
3.751178E+04, 3.847316E+04, 3.945398E+04, 4.045452E+04, 4.147505E+04, 4.251587E+04,
4.357726E+04, 4.465950E+04, 4.576289E+04, 4.688772E+04, 4.803426E+04, 4.920283E+04,
5.039369E+04, 5.160716E+04, 5.284352E+04, 5.410306E+04, 5.538609E+04, 5.669288E+04,
5.802374E+04, 5.937896E+04, 6.075884E+04, 6.216367E+04, 6.359374E+04, 6.504935E+04,
6.653080E+04, 6.803837E+04, 6.957236E+04, 7.113307E+04, 7.272078E+04, 7.433578E+04,
7.597838E+04, 7.764885E+04, 7.934749E+04, 8.107459E+04, 8.283042E+04, 8.461529E+04,
8.642947E+04, 8.827324E+04, 9.014689E+04, 9.205070E+04, 9.398494E+04, 9.594990E+04,
9.794585E+04, 9.997307E+04, 1.020318E+05, 1.041224E+05, 1.062450E+05, 1.084000E+05,
1.105876E+05, 1.128080E+05, 1.150616E+05, 1.173485E+05, 1.196691E+05, 1.220236E+05,
1.244122E+05, 1.268353E+05, 1.292929E+05, 1.317854E+05, 1.343131E+05, 1.368761E+05,
1.394747E+05, 1.421091E+05, 1.447796E+05, 1.474863E+05, 1.502295E+05, 1.530095E+05,
1.558264E+05, 1.586804E+05, 1.615718E+05, 1.645008E+05, 1.674676E+05, 1.704723E+05,
1.735152E+05, 1.765964E+05, 1.797163E+05, 1.828748E+05, 1.860723E+05, 1.893090E+05,
1.925849E+05, 1.959003E+05, 1.992553E+05, 2.026501E+05, 2.060849E+05, 2.095599E+05,
2.130751E+05, 2.166308E+05, 2.202271E+05, 2.238641E+05, 2.275421E+05, 2.312610E+05,
2.350212E+05, 2.388226E+05, 2.426655E+05, 2.465499E+05, 2.504760E+05, 2.544439E+05,
2.584538E+05, 2.625057E+05, 2.665997E+05, 2.707359E+05, 2.749145E+05, 2.791356E+05,
2.833992E+05, 2.877055E+05, 2.920544E+05, 2.964462E+05, 3.008809E+05, 3.053586E+05,
3.098792E+05, 3.144431E+05, 3.190500E+05, 3.237003E+05, 3.283938E+05, 3.331308E+05,
3.379111E+05, 3.427350E+05, 3.476023E+05, 3.525132E+05, 3.574677E+05, 3.624659E+05,
3.675078E+05, 3.725934E+05, 3.777227E+05, 3.828958E+05, 3.881127E+05, 3.933734E+05,
3.986779E+05, 4.040263E+05, 4.094186E+05, 4.148547E+05, 4.203346E+05, 4.258585E+05,
4.314262E+05, 4.370377E+05, 4.426932E+05, 4.483925E+05, 4.541356E+05, 4.599225E+05,
4.657532E+05, 4.716277E+05, 4.775460E+05, 4.835080E+05, 4.895137E+05,
])
# ---------------------- M = 1, I = 5 ---------------------------
M = 1
I = 5
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[0]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
6.000000E+00, 1.786086E+01, 4.625151E+01, 8.276956E+01, 1.258219E+02, 1.745467E+02,
2.283599E+02, 2.868320E+02, 3.496303E+02, 4.164889E+02, 4.871921E+02, 5.615653E+02,
6.394690E+02, 7.207956E+02, 8.054658E+02, 8.934261E+02, 9.846456E+02, 1.079113E+03,
1.176835E+03, 1.277832E+03, 1.382138E+03, 1.489795E+03, 1.600858E+03, 1.715385E+03,
1.833446E+03, 1.955112E+03, 2.080462E+03, 2.209579E+03, 2.342549E+03, 2.479463E+03,
2.620417E+03, 2.765507E+03, 2.914836E+03, 3.068509E+03, 3.226632E+03, 3.389316E+03,
3.556674E+03, 3.728824E+03, 3.905883E+03, 4.087973E+03, 4.275219E+03, 4.467747E+03,
4.665685E+03, 4.869166E+03, 5.078323E+03, 5.293293E+03, 5.514213E+03, 5.741224E+03,
5.974468E+03, 6.214091E+03, 6.460239E+03, 6.713060E+03, 6.972705E+03, 7.239327E+03,
7.513080E+03, 7.794120E+03, 8.082606E+03, 8.378697E+03, 8.682555E+03, 8.994343E+03,
9.314226E+03, 9.642370E+03, 9.978944E+03, 1.032412E+04, 1.067806E+04, 1.104095E+04,
1.141296E+04, 1.179427E+04, 1.218504E+04, 1.258547E+04, 1.299573E+04, 1.341601E+04,
1.384648E+04, 1.428733E+04, 1.473876E+04, 1.520094E+04, 1.567407E+04, 1.615834E+04,
1.665393E+04, 1.716105E+04, 1.767988E+04, 1.821063E+04, 1.875349E+04, 1.930866E+04,
1.987633E+04, 2.045672E+04, 2.105002E+04, 2.165643E+04, 2.227617E+04, 2.290944E+04,
2.355644E+04, 2.421738E+04, 2.489248E+04, 2.558195E+04, 2.628600E+04, 2.700484E+04,
2.773869E+04, 2.848776E+04, 2.925227E+04, 3.003245E+04, 3.082850E+04, 3.164066E+04,
3.246914E+04, 3.331417E+04, 3.417596E+04, 3.505476E+04, 3.595078E+04, 3.686425E+04,
3.779540E+04, 3.874447E+04, 3.971168E+04, 4.069726E+04, 4.170145E+04, 4.272449E+04,
4.376661E+04, 4.482804E+04, 4.590903E+04, 4.700981E+04, 4.813063E+04, 4.927172E+04,
5.043333E+04, 5.161570E+04, 5.281908E+04, 5.404371E+04, 5.528984E+04, 5.655772E+04,
5.784759E+04, 5.915970E+04, 6.049431E+04, 6.185167E+04, 6.323202E+04, 6.463563E+04,
6.606275E+04, 6.751363E+04, 6.898853E+04, 7.048770E+04, 7.201142E+04, 7.355993E+04,
7.513350E+04, 7.673238E+04, 7.835685E+04, 8.000716E+04, 8.168358E+04, 8.338637E+04,
8.511579E+04, 8.687212E+04, 8.865563E+04, 9.046658E+04, 9.230523E+04, 9.417187E+04,
9.606675E+04, 9.799016E+04, 9.994237E+04, 1.019236E+05, 1.039343E+05, 1.059745E+05,
1.080446E+05, 1.101449E+05, 1.122756E+05, 1.144371E+05, 1.166295E+05, 1.188533E+05,
1.211086E+05, 1.233957E+05, 1.257149E+05, 1.280666E+05, 1.304509E+05, 1.328682E+05,
1.353187E+05, 1.378027E+05, 1.403206E+05, 1.428725E+05, 1.454589E+05, 1.480799E+05,
1.507358E+05, 1.534269E+05, 1.561536E+05, 1.589161E+05, 1.617147E+05, 1.645496E+05,
1.674212E+05, 1.703297E+05, 1.732755E+05, 1.762588E+05, 1.792799E+05, 1.823391E+05,
1.854366E+05, 1.885729E+05, 1.917480E+05, 1.949625E+05, 1.982164E+05, 2.015102E+05,
2.048440E+05, 2.082182E+05, 2.116331E+05, 2.150890E+05, 2.185861E+05, 2.221247E+05,
2.257051E+05, 2.293277E+05, 2.329926E+05, 2.367001E+05, 2.404506E+05, 2.442444E+05,
2.480816E+05, 2.519626E+05, 2.558877E+05, 2.598572E+05, 2.638713E+05, 2.679303E+05,
2.720345E+05, 2.761841E+05, 2.803795E+05, 2.846209E+05, 2.889086E+05, 2.932429E+05,
2.976240E+05, 3.020522E+05, 3.065279E+05, 3.110511E+05, 3.156223E+05, 3.202417E+05,
3.249096E+05, 3.296262E+05, 3.343917E+05, 3.392066E+05, 3.440709E+05, 3.489851E+05,
3.539492E+05, 3.589637E+05, 3.640287E+05, 3.691446E+05, 3.743115E+05, 3.795298E+05,
3.847996E+05, 3.901213E+05, 3.954950E+05, 4.009211E+05, 4.063997E+05, 4.119311E+05,
4.175156E+05, 4.231535E+05, 4.288448E+05, 4.345899E+05, 4.403891E+05, 4.462425E+05,
4.521504E+05, 4.581130E+05, 4.641305E+05, 4.702033E+05, 4.763314E+05,
])
# ---------------------- M = 1, I = 6 ---------------------------
M = 1
I = 6
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[0]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
3.600000E+01, 1.066180E+02, 2.759495E+02, 4.937802E+02, 7.505847E+02, 1.041223E+03,
1.362217E+03, 1.711020E+03, 2.085674E+03, 2.484623E+03, 2.906617E+03, 3.350642E+03,
3.815891E+03, 4.301738E+03, 4.807723E+03, 5.333532E+03, 5.878982E+03, 6.444007E+03,
7.028640E+03, 7.633001E+03, 8.257283E+03, 8.901742E+03, 9.566686E+03, 1.025247E+04,
1.095949E+04, 1.168818E+04, 1.243899E+04, 1.321241E+04, 1.400896E+04, 1.482918E+04,
1.567361E+04, 1.654285E+04, 1.743748E+04, 1.835812E+04, 1.930541E+04, 2.027999E+04,
2.128254E+04, 2.231374E+04, 2.337427E+04, 2.446487E+04, 2.558624E+04, 2.673914E+04,
2.792432E+04, 2.914254E+04, 3.039459E+04, 3.168126E+04, 3.300335E+04, 3.436168E+04,
3.575707E+04, 3.719038E+04, 3.866244E+04, 4.017412E+04, 4.172629E+04, 4.331984E+04,
4.495565E+04, 4.663462E+04, 4.835768E+04, 5.012574E+04, 5.193973E+04, 5.380059E+04,
5.570927E+04, 5.766673E+04, 5.967393E+04, 6.173186E+04, 6.384149E+04, 6.600381E+04,
6.821984E+04, 7.049057E+04, 7.281703E+04, 7.520023E+04, 7.764122E+04, 8.014104E+04,
8.270072E+04, 8.532134E+04, 8.800395E+04, 9.074963E+04, 9.355945E+04, 9.643451E+04,
9.937589E+04, 1.023847E+05, 1.054620E+05, 1.086090E+05, 1.118268E+05, 1.151165E+05,
1.184792E+05, 1.219162E+05, 1.254284E+05, 1.290172E+05, 1.326836E+05, 1.364289E+05,
1.402542E+05, 1.441607E+05, 1.481496E+05, 1.522221E+05, 1.563793E+05, 1.606226E+05,
1.649532E+05, 1.693722E+05, 1.738809E+05, 1.784805E+05, 1.831723E+05, 1.879575E+05,
1.928374E+05, 1.978133E+05, 2.028864E+05, 2.080579E+05, 2.133293E+05, 2.187018E+05,
2.241766E+05, 2.297550E+05, 2.354385E+05, 2.412283E+05, 2.471256E+05, 2.531319E+05,
2.592485E+05, 2.654767E+05, 2.718178E+05, 2.782733E+05, 2.848444E+05, 2.915325E+05,
2.983391E+05, 3.052654E+05, 3.123128E+05, 3.194828E+05, 3.267767E+05, 3.341959E+05,
3.417418E+05, 3.494159E+05, 3.572195E+05, 3.651541E+05, 3.732210E+05, 3.814218E+05,
3.897578E+05, 3.982305E+05, 4.068414E+05, 4.155918E+05, 4.244833E+05, 4.335172E+05,
4.426952E+05, 4.520186E+05, 4.614889E+05, 4.711076E+05, 4.808762E+05, 4.907962E+05,
5.008691E+05, 5.110963E+05, 5.214794E+05, 5.320199E+05, 5.427193E+05, 5.535792E+05,
5.646009E+05, 5.757862E+05, 5.871364E+05, 5.986532E+05, 6.103381E+05, 6.221926E+05,
6.342182E+05, 6.464166E+05, 6.587892E+05, 6.713377E+05, 6.840635E+05, 6.969684E+05,
7.100537E+05, 7.233211E+05, 7.367722E+05, 7.504086E+05, 7.642317E+05, 7.782433E+05,
7.924449E+05, 8.068380E+05, 8.214243E+05, 8.362053E+05, 8.511827E+05, 8.663581E+05,
8.817330E+05, 8.973090E+05, 9.130878E+05, 9.290709E+05, 9.452599E+05, 9.616565E+05,
9.782622E+05, 9.950787E+05, 1.012108E+06, 1.029350E+06, 1.046809E+06, 1.064484E+06,
1.082379E+06, 1.100493E+06, 1.118830E+06, 1.137390E+06, 1.156176E+06, 1.175188E+06,
1.194429E+06, 1.213900E+06, 1.233602E+06, 1.253538E+06, 1.273708E+06, 1.294115E+06,
1.314760E+06, 1.335644E+06, 1.356770E+06, 1.378138E+06, 1.399750E+06, 1.421609E+06,
1.443715E+06, 1.466070E+06, 1.488676E+06, 1.511534E+06, 1.534646E+06, 1.558013E+06,
1.581636E+06, 1.605519E+06, 1.629661E+06, 1.654065E+06, 1.678732E+06, 1.703663E+06,
1.728861E+06, 1.754326E+06, 1.780061E+06, 1.806066E+06, 1.832343E+06, 1.858894E+06,
1.885720E+06, 1.912822E+06, 1.940203E+06, 1.967863E+06, 1.995805E+06, 2.024028E+06,
2.052536E+06, 2.081329E+06, 2.110410E+06, 2.139778E+06, 2.169436E+06, 2.199385E+06,
2.229627E+06, 2.260162E+06, 2.290993E+06, 2.322120E+06, 2.353546E+06, 2.385271E+06,
2.417296E+06, 2.449624E+06, 2.482255E+06, 2.515191E+06, 2.548433E+06, 2.581982E+06,
2.615840E+06, 2.650008E+06, 2.684487E+06, 2.719279E+06, 2.754384E+06,
])
# ---------------------- M = 1, I = 7 ---------------------------
M = 1
I = 7
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[1]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
6.000000E+00, 2.020566E+01, 5.328177E+01, 9.597421E+01, 1.463666E+02, 2.034368E+02,
2.664953E+02, 3.350360E+02, 4.086708E+02, 4.870968E+02, 5.700805E+02, 6.574474E+02,
7.490750E+02, 8.448862E+02, 9.448428E+02, 1.048939E+03, 1.157194E+03, 1.269651E+03,
1.386368E+03, 1.507418E+03, 1.632887E+03, 1.762868E+03, 1.897464E+03, 2.036785E+03,
2.180948E+03, 2.330078E+03, 2.484305E+03, 2.643764E+03, 2.808599E+03, 2.978957E+03,
3.154991E+03, 3.336863E+03, 3.524736E+03, 3.718782E+03, 3.919176E+03, 4.126099E+03,
4.339738E+03, 4.560283E+03, 4.787929E+03, 5.022879E+03, 5.265336E+03, 5.515511E+03,
5.773617E+03, 6.039873E+03, 6.314501E+03, 6.597728E+03, 6.889787E+03, 7.190910E+03,
7.501338E+03, 7.821315E+03, 8.151087E+03, 8.490906E+03, 8.841027E+03, 9.201709E+03,
9.573217E+03, 9.955817E+03, 1.034978E+04, 1.075538E+04, 1.117291E+04, 1.160263E+04,
1.204485E+04, 1.249984E+04, 1.296792E+04, 1.344937E+04, 1.394451E+04, 1.445364E+04,
1.497708E+04, 1.551513E+04, 1.606813E+04, 1.663640E+04, 1.722027E+04, 1.782008E+04,
1.843615E+04, 1.906885E+04, 1.971852E+04, 2.038550E+04, 2.107016E+04, 2.177286E+04,
2.249397E+04, 2.323386E+04, 2.399290E+04, 2.477148E+04, 2.556999E+04, 2.638881E+04,
2.722834E+04, 2.808898E+04, 2.897114E+04, 2.987523E+04, 3.080167E+04, 3.175088E+04,
3.272328E+04, 3.371931E+04, 3.473940E+04, 3.578400E+04, 3.685355E+04, 3.794852E+04,
3.906935E+04, 4.021651E+04, 4.139047E+04, 4.259171E+04, 4.382070E+04, 4.507794E+04,
4.636392E+04, 4.767913E+04, 4.902407E+04, 5.039927E+04, 5.180522E+04, 5.324247E+04,
5.471152E+04, 5.621291E+04, 5.774719E+04, 5.931490E+04, 6.091658E+04, 6.255280E+04,
6.422412E+04, 6.593111E+04, 6.767435E+04, 6.945442E+04, 7.127190E+04, 7.312739E+04,
7.502150E+04, 7.695482E+04, 7.892798E+04, 8.094160E+04, 8.299630E+04, 8.509271E+04,
8.723149E+04, 8.941326E+04, 9.163870E+04, 9.390846E+04, 9.622320E+04, 9.858361E+04,
1.009904E+05, 1.034441E+05, 1.059457E+05, 1.084956E+05, 1.110947E+05, 1.137436E+05,
1.164431E+05, 1.191939E+05, 1.219968E+05, 1.248525E+05, 1.277617E+05, 1.307252E+05,
1.337438E+05, 1.368183E+05, 1.399493E+05, 1.431378E+05, 1.463845E+05, 1.496903E+05,
1.530558E+05, 1.564820E+05, 1.599697E+05, 1.635197E+05, 1.671329E+05, 1.708101E+05,
1.745521E+05, 1.783599E+05, 1.822343E+05, 1.861762E+05, 1.901864E+05, 1.942659E+05,
1.984156E+05, 2.026364E+05, 2.069291E+05, 2.112949E+05, 2.157345E+05, 2.202489E+05,
2.248391E+05, 2.295060E+05, 2.342506E+05, 2.390739E+05, 2.439768E+05, 2.489604E+05,
2.540256E+05, 2.591735E+05, 2.644051E+05, 2.697213E+05, 2.751233E+05, 2.806120E+05,
2.861886E+05, 2.918540E+05, 2.976093E+05, 3.034557E+05, 3.093941E+05, 3.154257E+05,
3.215515E+05, 3.277727E+05, 3.340904E+05, 3.405057E+05, 3.470196E+05, 3.536334E+05,
3.603482E+05, 3.671652E+05, 3.740854E+05, 3.811100E+05, 3.882403E+05, 3.954774E+05,
4.028224E+05, 4.102766E+05, 4.178412E+05, 4.255174E+05, 4.333064E+05, 4.412094E+05,
4.492276E+05, 4.573623E+05, 4.656147E+05, 4.739861E+05, 4.824777E+05, 4.910907E+05,
4.998266E+05, 5.086864E+05, 5.176716E+05, 5.267833E+05, 5.360229E+05, 5.453917E+05,
5.548910E+05, 5.645222E+05, 5.742864E+05, 5.841851E+05, 5.942195E+05, 6.043911E+05,
6.147011E+05, 6.251509E+05, 6.357419E+05, 6.464753E+05, 6.573526E+05, 6.683752E+05,
6.795444E+05, 6.908615E+05, 7.023280E+05, 7.139453E+05, 7.257147E+05, 7.376376E+05,
7.497155E+05, 7.619497E+05, 7.743416E+05, 7.868928E+05, 7.996044E+05, 8.124781E+05,
8.255152E+05, 8.387171E+05, 8.520852E+05, 8.656211E+05, 8.793261E+05, 8.932017E+05,
9.072493E+05, 9.214703E+05, 9.358662E+05, 9.504385E+05, 9.651886E+05, 9.801180E+05,
9.952280E+05, 1.010520E+06, 1.025996E+06, 1.041657E+06, 1.057504E+06, 1.073540E+06,
1.089765E+06, 1.106181E+06, 1.122789E+06, 1.139591E+06, 1.156589E+06, 1.173783E+06,
1.191175E+06, 1.208768E+06, 1.226561E+06, 1.244557E+06, 1.262758E+06, 1.281164E+06,
1.299777E+06, 1.318598E+06, 1.337630E+06, 1.356873E+06, 1.376329E+06, 1.396000E+06,
1.415886E+06, 1.435990E+06, 1.456312E+06, 1.476855E+06, 1.497619E+06, 1.518607E+06,
1.539819E+06, 1.561257E+06, 1.582923E+06, 1.604818E+06, 1.626943E+06, 1.649300E+06,
1.671891E+06, 1.694716E+06, 1.717777E+06, 1.741076E+06, 1.764614E+06, 1.788392E+06,
1.812412E+06, 1.836675E+06, 1.861184E+06, 1.885938E+06, 1.910939E+06, 1.936190E+06,
1.961691E+06,
])
# ---------------------- M = 1, I = 8 ---------------------------
M = 1
I = 8
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[1]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
6.000000E+00, 2.048931E+01, 5.406267E+01, 9.739573E+01, 1.485457E+02, 2.064750E+02,
2.704833E+02, 3.400571E+02, 4.148027E+02, 4.944135E+02, 5.786535E+02, 6.673477E+02,
7.603741E+02, 8.576570E+02, 9.591602E+02, 1.064880E+03, 1.174840E+03, 1.289086E+03,
1.407678E+03, 1.530695E+03, 1.658222E+03, 1.790360E+03, 1.927213E+03, 2.068896E+03,
2.215529E+03, 2.367241E+03, 2.524165E+03, 2.686441E+03, 2.854217E+03, 3.027645E+03,
3.206883E+03, 3.392094E+03, 3.583450E+03, 3.781124E+03, 3.985298E+03, 4.196157E+03,
4.413893E+03, 4.638702E+03, 4.870784E+03, 5.110345E+03, 5.357596E+03, 5.612751E+03,
5.876031E+03, 6.147658E+03, 6.427862E+03, 6.716875E+03, 7.014934E+03, 7.322280E+03,
7.639158E+03, 7.965818E+03, 8.302513E+03, 8.649501E+03, 9.007044E+03, 9.375407E+03,
9.754862E+03, 1.014568E+04, 1.054814E+04, 1.096253E+04, 1.138913E+04, 1.182824E+04,
1.228015E+04, 1.274515E+04, 1.322355E+04, 1.371567E+04, 1.422181E+04, 1.474229E+04,
1.527743E+04, 1.582755E+04, 1.639299E+04, 1.697409E+04, 1.757117E+04, 1.818460E+04,
1.881470E+04, 1.946184E+04, 2.012638E+04, 2.080867E+04, 2.150909E+04, 2.222801E+04,
2.296581E+04, 2.372286E+04, 2.449955E+04, 2.529629E+04, 2.611346E+04, 2.695146E+04,
2.781071E+04, 2.869162E+04, 2.959460E+04, 3.052008E+04, 3.146849E+04, 3.244026E+04,
3.343582E+04, 3.445564E+04, 3.550014E+04, 3.656980E+04, 3.766507E+04, 3.878642E+04,
3.993432E+04, 4.110925E+04, 4.231169E+04, 4.354213E+04, 4.480107E+04, 4.608901E+04,
4.740645E+04, 4.875391E+04, 5.013191E+04, 5.154097E+04, 5.298163E+04, 5.445442E+04,
5.595988E+04, 5.749856E+04, 5.907103E+04, 6.067783E+04, 6.231955E+04, 6.399675E+04,
6.571001E+04, 6.745993E+04, 6.924709E+04, 7.107210E+04, 7.293556E+04, 7.483809E+04,
7.678030E+04, 7.876284E+04, 8.078632E+04, 8.285139E+04, 8.495869E+04, 8.710888E+04,
8.930263E+04, 9.154060E+04, 9.382346E+04, 9.615189E+04, 9.852659E+04, 1.009483E+05,
1.034176E+05, 1.059353E+05, 1.085021E+05, 1.111187E+05, 1.137859E+05, 1.165043E+05,
1.192748E+05, 1.220981E+05, 1.249750E+05, 1.279061E+05, 1.308924E+05, 1.339345E+05,
1.370334E+05, 1.401897E+05, 1.434043E+05, 1.466780E+05, 1.500116E+05, 1.534060E+05,
1.568620E+05, 1.603804E+05, 1.639621E+05, 1.676080E+05, 1.713189E+05, 1.750957E+05,
1.789393E+05, 1.828506E+05, 1.868305E+05, 1.908799E+05, 1.949997E+05, 1.991908E+05,
2.034543E+05, 2.077909E+05, 2.122018E+05, 2.166877E+05, 2.212498E+05, 2.258889E+05,
2.306061E+05, 2.354024E+05, 2.402787E+05, 2.452361E+05, 2.502755E+05, 2.553980E+05,
2.606047E+05, 2.658966E+05, 2.712746E+05, 2.767400E+05, 2.822937E+05, 2.879368E+05,
2.936704E+05, 2.994957E+05, 3.054136E+05, 3.114253E+05, 3.175319E+05, 3.237345E+05,
3.300344E+05, 3.364325E+05, 3.429300E+05, 3.495282E+05, 3.562281E+05, 3.630310E+05,
3.699380E+05, 3.769503E+05, 3.840690E+05, 3.912955E+05, 3.986309E+05, 4.060763E+05,
4.136332E+05, 4.213026E+05, 4.290858E+05, 4.369840E+05, 4.449986E+05, 4.531307E+05,
4.613817E+05, 4.697528E+05, 4.782453E+05, 4.868605E+05, 4.955996E+05, 5.044641E+05,
5.134551E+05, 5.225741E+05, 5.318222E+05, 5.412010E+05, 5.507116E+05, 5.603555E+05,
5.701339E+05, 5.800483E+05, 5.901000E+05, 6.002904E+05, 6.106208E+05, 6.210926E+05,
6.317072E+05, 6.424660E+05, 6.533704E+05, 6.644218E+05, 6.756215E+05, 6.869711E+05,
6.984719E+05, 7.101253E+05, 7.219327E+05, 7.338957E+05, 7.460156E+05, 7.582938E+05,
7.707319E+05, 7.833312E+05, 7.960932E+05, 8.090193E+05, 8.221111E+05, 8.353700E+05,
8.487974E+05, 8.623948E+05, 8.761637E+05, 8.901056E+05, 9.042219E+05, 9.185141E+05,
9.329838E+05, 9.476323E+05, 9.624612E+05, 9.774720E+05, 9.926662E+05, 1.008045E+06,
1.023611E+06, 1.039364E+06, 1.055307E+06, 1.071440E+06, 1.087766E+06, 1.104286E+06,
1.121001E+06, 1.137913E+06, 1.155024E+06, 1.172334E+06, 1.189846E+06, 1.207561E+06,
1.225480E+06, 1.243606E+06, 1.261938E+06, 1.280480E+06, 1.299233E+06, 1.318197E+06,
1.337375E+06, 1.356767E+06, 1.376377E+06, 1.396204E+06, 1.416251E+06, 1.436519E+06,
1.457010E+06, 1.477725E+06, 1.498665E+06, 1.519833E+06, 1.541229E+06, 1.562855E+06,
1.584712E+06, 1.606803E+06, 1.629128E+06, 1.651689E+06, 1.674488E+06, 1.697526E+06,
1.720804E+06, 1.744324E+06, 1.768088E+06, 1.792097E+06, 1.816351E+06, 1.840854E+06,
1.865606E+06, 1.890609E+06, 1.915864E+06, 1.941372E+06, 1.967136E+06, 1.993156E+06,
2.019434E+06,
])
# ---------------------- M = 1, I = 9 ---------------------------
M = 1
I = 9
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[1]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
3.600000E+01, 1.221318E+02, 3.221607E+02, 5.803382E+02, 8.850833E+02, 1.230215E+03,
1.611562E+03, 2.026065E+03, 2.471377E+03, 2.945671E+03, 3.447538E+03, 3.975930E+03,
4.530112E+03, 5.109629E+03, 5.714256E+03, 6.343965E+03, 6.998887E+03, 7.679283E+03,
8.385518E+03, 9.118039E+03, 9.877365E+03, 1.066407E+04, 1.147879E+04, 1.232218E+04,
1.319497E+04, 1.409792E+04, 1.503180E+04, 1.599746E+04, 1.699576E+04, 1.802760E+04,
1.909392E+04, 2.019568E+04, 2.133390E+04, 2.250960E+04, 2.372386E+04, 2.497778E+04,
2.627249E+04, 2.760915E+04, 2.898894E+04, 3.041308E+04, 3.188283E+04, 3.339945E+04,
3.496424E+04, 3.657853E+04, 3.824366E+04, 3.996102E+04, 4.173201E+04, 4.355806E+04,
4.544061E+04, 4.738114E+04, 4.938116E+04, 5.144219E+04, 5.356578E+04, 5.575351E+04,
5.800696E+04, 6.032778E+04, 6.271759E+04, 6.517809E+04, 6.771096E+04, 7.031792E+04,
7.300073E+04, 7.576115E+04, 7.860099E+04, 8.152207E+04, 8.452624E+04, 8.761536E+04,
9.079135E+04, 9.405614E+04, 9.741167E+04, 1.008599E+05, 1.044029E+05, 1.080427E+05,
1.117813E+05, 1.156208E+05, 1.195634E+05, 1.236111E+05, 1.277662E+05, 1.320309E+05,
1.364074E+05, 1.408980E+05, 1.455049E+05, 1.502304E+05, 1.550770E+05, 1.600471E+05,
1.651429E+05, 1.703670E+05, 1.757218E+05, 1.812099E+05, 1.868337E+05, 1.925958E+05,
1.984989E+05, 2.045456E+05, 2.107385E+05, 2.170803E+05, 2.235737E+05, 2.302216E+05,
2.370267E+05, 2.439918E+05, 2.511198E+05, 2.584135E+05, 2.658760E+05, 2.735102E+05,
2.813191E+05, 2.893056E+05, 2.974730E+05, 3.058242E+05, 3.143624E+05, 3.230909E+05,
3.320128E+05, 3.411313E+05, 3.504498E+05, 3.599715E+05, 3.696999E+05, 3.796383E+05,
3.897902E+05, 4.001590E+05, 4.107482E+05, 4.215615E+05, 4.326023E+05, 4.438744E+05,
4.553813E+05, 4.671268E+05, 4.791146E+05, 4.913485E+05, 5.038324E+05, 5.165701E+05,
5.295655E+05, 5.428226E+05, 5.563453E+05, 5.701377E+05, 5.842039E+05, 5.985480E+05,
6.131740E+05, 6.280863E+05, 6.432891E+05, 6.587866E+05, 6.745832E+05, 6.906833E+05,
7.070913E+05, 7.238115E+05, 7.408486E+05, 7.582071E+05, 7.758916E+05, 7.939067E+05,
8.122571E+05, 8.309475E+05, 8.499827E+05, 8.693675E+05, 8.891068E+05, 9.092054E+05,
9.296684E+05, 9.505008E+05, 9.717075E+05, 9.932938E+05, 1.015265E+06, 1.037625E+06,
1.060381E+06, 1.083537E+06, 1.107099E+06, 1.131072E+06, 1.155461E+06, 1.180272E+06,
1.205511E+06, 1.231183E+06, 1.257293E+06, 1.283848E+06, 1.310853E+06, 1.338313E+06,
1.366236E+06, 1.394625E+06, 1.423488E+06, 1.452831E+06, 1.482659E+06, 1.512978E+06,
1.543794E+06, 1.575115E+06, 1.606945E+06, 1.639291E+06, 1.672160E+06, 1.705557E+06,
1.739489E+06, 1.773963E+06, 1.808986E+06, 1.844562E+06, 1.880700E+06, 1.917406E+06,
1.954686E+06, 1.992547E+06, 2.030996E+06, 2.070041E+06, 2.109686E+06, 2.149941E+06,
2.190810E+06, 2.232303E+06, 2.274425E+06, 2.317183E+06, 2.360586E+06, 2.404639E+06,
2.449351E+06, 2.494728E+06, 2.540778E+06, 2.587508E+06, 2.634926E+06, 2.683039E+06,
2.731855E+06, 2.781380E+06, 2.831623E+06, 2.882592E+06, 2.934293E+06, 2.986735E+06,
3.039925E+06, 3.093872E+06, 3.148582E+06, 3.204065E+06, 3.260327E+06, 3.317376E+06,
3.375221E+06, 3.433870E+06, 3.493331E+06, 3.553611E+06, 3.614719E+06, 3.676663E+06,
3.739451E+06, 3.803092E+06, 3.867593E+06, 3.932963E+06, 3.999210E+06, 4.066343E+06,
4.134370E+06, 4.203299E+06, 4.273139E+06, 4.343898E+06, 4.415584E+06, 4.488207E+06,
4.561774E+06, 4.636295E+06, 4.711778E+06, 4.788230E+06, 4.865663E+06, 4.944082E+06,
5.023498E+06, 5.103920E+06, 5.185355E+06, 5.267812E+06, 5.351301E+06, 5.435830E+06,
5.521408E+06, 5.608043E+06, 5.695745E+06, 5.784521E+06, 5.874383E+06, 5.965337E+06,
6.057392E+06, 6.150559E+06, 6.244845E+06, 6.340260E+06, 6.436812E+06, 6.534510E+06,
6.633364E+06, 6.733382E+06, 6.834573E+06, 6.936945E+06, 7.040509E+06, 7.145273E+06,
7.251246E+06, 7.358436E+06, 7.466853E+06, 7.576506E+06, 7.687404E+06, 7.799555E+06,
7.912969E+06, 8.027654E+06, 8.143620E+06, 8.260875E+06, 8.379428E+06, 8.499289E+06,
8.620465E+06, 8.742967E+06, 8.866803E+06, 8.991981E+06, 9.118511E+06, 9.246401E+06,
9.375661E+06, 9.506299E+06, 9.638324E+06, 9.771745E+06, 9.906570E+06, 1.004281E+07,
1.018047E+07, 1.031956E+07, 1.046009E+07, 1.060207E+07, 1.074551E+07, 1.089041E+07,
1.103679E+07, 1.118465E+07, 1.133400E+07, 1.148485E+07, 1.163721E+07, 1.179108E+07,
1.194648E+07,
])
# ---------------------- M = 2, I = 1 ---------------------------
M = 2
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[0]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.172300E+00, 1.797937E+01, 3.579152E+01, 5.360487E+01, 7.142001E+01, 8.924629E+01,
1.071216E+02, 1.251286E+02, 1.433912E+02, 1.620592E+02, 1.812909E+02, 2.012420E+02,
2.220599E+02, 2.438818E+02, 2.668355E+02, 2.910405E+02, 3.166103E+02, 3.436540E+02,
3.722775E+02, 4.025853E+02, 4.346809E+02, 4.686683E+02, 5.046524E+02, 5.427394E+02,
5.830375E+02, 6.256573E+02, 6.707119E+02, 7.183172E+02, 7.685924E+02, 8.216598E+02,
8.776450E+02, 9.366774E+02, 9.988899E+02, 1.064419E+03, 1.133406E+03, 1.205995E+03,
1.282335E+03, 1.362579E+03, 1.446883E+03, 1.535408E+03, 1.628322E+03, 1.725793E+03,
1.827996E+03, 1.935111E+03, 2.047321E+03, 2.164814E+03, 2.287785E+03, 2.416430E+03,
2.550954E+03, 2.691564E+03, 2.838473E+03, 2.991899E+03, 3.152065E+03, 3.319200E+03,
3.493538E+03, 3.675317E+03, 3.864782E+03, 4.062182E+03, 4.267774E+03, 4.481819E+03,
4.704582E+03, 4.936336E+03, 5.177359E+03, 5.427935E+03, 5.688354E+03, 5.958911E+03,
6.239909E+03, 6.531654E+03, 6.834460E+03, 7.148647E+03, 7.474541E+03, 7.812474E+03,
8.162786E+03, 8.525820E+03, 8.901928E+03, 9.291468E+03, 9.694803E+03, 1.011231E+04,
1.054435E+04, 1.099132E+04, 1.145362E+04, 1.193162E+04, 1.242575E+04, 1.293641E+04,
1.346401E+04, 1.400899E+04, 1.457177E+04, 1.515280E+04, 1.575251E+04, 1.637136E+04,
1.700982E+04, 1.766834E+04, 1.834740E+04, 1.904748E+04, 1.976907E+04, 2.051267E+04,
2.127878E+04, 2.206790E+04, 2.288057E+04, 2.371729E+04, 2.457861E+04, 2.546506E+04,
2.637719E+04, 2.731556E+04, 2.828073E+04, 2.927327E+04, 3.029376E+04, 3.134277E+04,
3.242091E+04, 3.352878E+04, 3.466698E+04, 3.583614E+04, 3.703686E+04, 3.826979E+04,
3.953557E+04, 4.083483E+04, 4.216825E+04, 4.353647E+04, 4.494018E+04, 4.638004E+04,
4.785674E+04, 4.937098E+04, 5.092347E+04, 5.251490E+04, 5.414600E+04, 5.581749E+04,
5.753010E+04, 5.928458E+04, 6.108167E+04, 6.292213E+04, 6.480672E+04, 6.673621E+04,
6.871139E+04, 7.073304E+04, 7.280195E+04, 7.491893E+04, 7.708478E+04, 7.930032E+04,
8.156638E+04, 8.388379E+04, 8.625338E+04, 8.867600E+04, 9.115251E+04, 9.368376E+04,
9.627062E+04, 9.891397E+04, 1.016147E+05, 1.043737E+05, 1.071918E+05, 1.100700E+05,
1.130091E+05, 1.160101E+05, 1.190739E+05, 1.222014E+05, 1.253936E+05, 1.286514E+05,
1.319757E+05, 1.353675E+05, 1.388278E+05, 1.423574E+05, 1.459575E+05, 1.496289E+05,
1.533726E+05, 1.571897E+05, 1.610810E+05, 1.650477E+05, 1.690906E+05, 1.732109E+05,
1.774094E+05, 1.816873E+05, 1.860455E+05, 1.904851E+05, 1.950070E+05, 1.996124E+05,
2.043022E+05, 2.090774E+05, 2.139392E+05, 2.188885E+05, 2.239265E+05, 2.290540E+05,
2.342723E+05, 2.395823E+05, 2.449850E+05, 2.504817E+05, 2.560732E+05, 2.617607E+05,
2.675453E+05, 2.734279E+05, 2.794097E+05, 2.854917E+05, 2.916749E+05, 2.979606E+05,
3.043496E+05, 3.108431E+05, 3.174422E+05, 3.241479E+05, 3.309613E+05, 3.378834E+05,
3.449153E+05, 3.520581E+05, 3.593129E+05, 3.666806E+05, 3.741624E+05, 3.817594E+05,
3.894725E+05, 3.973029E+05, 4.052515E+05, 4.133195E+05, 4.215079E+05, 4.298178E+05,
4.382501E+05, 4.468060E+05, 4.554865E+05, 4.642925E+05, 4.732252E+05, 4.822856E+05,
4.914747E+05, 5.007935E+05, 5.102430E+05, 5.198243E+05, 5.295383E+05, 5.393861E+05,
5.493687E+05, 5.594870E+05, 5.697421E+05, 5.801349E+05, 5.906665E+05, 6.013377E+05,
6.121497E+05, 6.231032E+05, 6.341994E+05, 6.454391E+05, 6.568232E+05, 6.683529E+05,
6.800289E+05, 6.918522E+05, 7.038237E+05, 7.159444E+05, 7.282151E+05, 7.406368E+05,
7.532103E+05, 7.659366E+05, 7.788165E+05, 7.918509E+05, 8.050406E+05, 8.183866E+05,
8.318897E+05, 8.455507E+05, 8.593704E+05, 8.733498E+05, 8.874895E+05,
])
# ---------------------- M = 2, I = 2 ---------------------------
M = 2
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[0]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
2.344550E+00, 3.595709E+01, 7.157976E+01, 1.072048E+02, 1.428342E+02, 1.784919E+02,
2.142697E+02, 2.503579E+02, 2.870319E+02, 3.246141E+02, 3.634386E+02, 4.038283E+02,
4.460854E+02, 4.904894E+02, 5.372995E+02, 5.867589E+02, 6.390984E+02, 6.945407E+02,
7.533034E+02, 8.156015E+02, 8.816500E+02, 9.516650E+02, 1.025865E+03, 1.104474E+03,
1.187719E+03, 1.275832E+03, 1.369052E+03, 1.467626E+03, 1.571805E+03, 1.681848E+03,
1.798023E+03, 1.920605E+03, 2.049877E+03, 2.186128E+03, 2.329660E+03, 2.480780E+03,
2.639806E+03, 2.807062E+03, 2.982884E+03, 3.167617E+03, 3.361614E+03, 3.565239E+03,
3.778863E+03, 4.002871E+03, 4.237655E+03, 4.483619E+03, 4.741175E+03, 5.010747E+03,
5.292769E+03, 5.587687E+03, 5.895957E+03, 6.218045E+03, 6.554429E+03, 6.905599E+03,
7.272055E+03, 7.654309E+03, 8.052885E+03, 8.468319E+03, 8.901159E+03, 9.351963E+03,
9.821303E+03, 1.030976E+04, 1.081794E+04, 1.134645E+04, 1.189590E+04, 1.246693E+04,
1.306020E+04, 1.367635E+04, 1.431607E+04, 1.498004E+04, 1.566896E+04, 1.638355E+04,
1.712453E+04, 1.789264E+04, 1.868864E+04, 1.951329E+04, 2.036739E+04, 2.125172E+04,
2.216710E+04, 2.311436E+04, 2.409433E+04, 2.510787E+04, 2.615584E+04, 2.723914E+04,
2.835866E+04, 2.951531E+04, 3.071003E+04, 3.194376E+04, 3.321745E+04, 3.453208E+04,
3.588864E+04, 3.728814E+04, 3.873160E+04, 4.022005E+04, 4.175454E+04, 4.333615E+04,
4.496596E+04, 4.664506E+04, 4.837457E+04, 5.015563E+04, 5.198938E+04, 5.387698E+04,
5.581962E+04, 5.781850E+04, 5.987482E+04, 6.198981E+04, 6.416473E+04, 6.640083E+04,
6.869940E+04, 7.106172E+04, 7.348912E+04, 7.598292E+04, 7.854447E+04, 8.117512E+04,
8.387627E+04, 8.664930E+04, 8.949564E+04, 9.241670E+04, 9.541394E+04, 9.848883E+04,
1.016428E+05, 1.048775E+05, 1.081942E+05, 1.115947E+05, 1.150803E+05, 1.186528E+05,
1.223136E+05, 1.260644E+05, 1.299067E+05, 1.338423E+05, 1.378728E+05, 1.419997E+05,
1.462249E+05, 1.505501E+05, 1.549768E+05, 1.595069E+05, 1.641422E+05, 1.688843E+05,
1.737350E+05, 1.786963E+05, 1.837698E+05, 1.889574E+05, 1.942609E+05, 1.996823E+05,
2.052233E+05, 2.108859E+05, 2.166720E+05, 2.225835E+05, 2.286223E+05, 2.347904E+05,
2.410897E+05, 2.475223E+05, 2.540900E+05, 2.607950E+05, 2.676393E+05, 2.746248E+05,
2.817536E+05, 2.890278E+05, 2.964495E+05, 3.040207E+05, 3.117435E+05, 3.196200E+05,
3.276524E+05, 3.358427E+05, 3.441932E+05, 3.527060E+05, 3.613832E+05, 3.702270E+05,
3.792395E+05, 3.884231E+05, 3.977798E+05, 4.073119E+05, 4.170216E+05, 4.269111E+05,
4.369827E+05, 4.472386E+05, 4.576810E+05, 4.683123E+05, 4.791346E+05, 4.901502E+05,
5.013614E+05, 5.127705E+05, 5.243797E+05, 5.361914E+05, 5.482078E+05, 5.604312E+05,
5.728640E+05, 5.855084E+05, 5.983667E+05, 6.114413E+05, 6.247344E+05, 6.382484E+05,
6.519855E+05, 6.659481E+05, 6.801385E+05, 6.945590E+05, 7.092119E+05, 7.240996E+05,
7.392243E+05, 7.545883E+05, 7.701940E+05, 7.860436E+05, 8.021395E+05, 8.184840E+05,
8.350793E+05, 8.519277E+05, 8.690316E+05, 8.863932E+05, 9.040148E+05, 9.218986E+05,
9.400470E+05, 9.584622E+05, 9.771464E+05, 9.961019E+05, 1.015331E+06, 1.034836E+06,
1.054619E+06, 1.074682E+06, 1.095027E+06, 1.115657E+06, 1.136574E+06, 1.157780E+06,
1.179277E+06, 1.201067E+06, 1.223153E+06, 1.245537E+06, 1.268220E+06, 1.291205E+06,
1.314494E+06, 1.338089E+06, 1.361993E+06, 1.386206E+06, 1.410732E+06, 1.435573E+06,
1.460729E+06, 1.486204E+06, 1.512000E+06, 1.538118E+06, 1.564560E+06, 1.591328E+06,
1.618425E+06, 1.645852E+06, 1.673611E+06, 1.701705E+06, 1.730134E+06, 1.758901E+06,
1.788007E+06, 1.817455E+06, 1.847247E+06, 1.877383E+06, 1.907867E+06,
])
# ---------------------- M = 2, I = 3 ---------------------------
M = 2
I = 3
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
2.260550E+00, 3.809072E+01, 7.584727E+01, 1.136063E+02, 1.513692E+02, 1.891574E+02,
2.270552E+02, 2.652444E+02, 3.039959E+02, 3.436341E+02, 3.845016E+02, 4.269345E+02,
4.712510E+02, 5.177476E+02, 5.667010E+02, 6.183711E+02, 6.730046E+02, 7.308391E+02,
7.921059E+02, 8.570332E+02, 9.258477E+02, 9.987770E+02, 1.076051E+03, 1.157902E+03,
1.244567E+03, 1.336290E+03, 1.433318E+03, 1.535906E+03, 1.644315E+03, 1.758814E+03,
1.879680E+03, 2.007196E+03, 2.141654E+03, 2.283356E+03, 2.432611E+03, 2.589737E+03,
2.755060E+03, 2.928918E+03, 3.111655E+03, 3.303627E+03, 3.505199E+03, 3.716746E+03,
3.938651E+03, 4.171310E+03, 4.415128E+03, 4.670521E+03, 4.937913E+03, 5.217743E+03,
5.510457E+03, 5.816514E+03, 6.136385E+03, 6.470548E+03, 6.819498E+03, 7.183738E+03,
7.563783E+03, 7.960160E+03, 8.373410E+03, 8.804082E+03, 9.252740E+03, 9.719961E+03,
1.020633E+04, 1.071245E+04, 1.123894E+04, 1.178641E+04, 1.235551E+04, 1.294689E+04,
1.356122E+04, 1.419917E+04, 1.486143E+04, 1.554872E+04, 1.626173E+04, 1.700122E+04,
1.776792E+04, 1.856258E+04, 1.938600E+04, 2.023894E+04, 2.112221E+04, 2.203662E+04,
2.298300E+04, 2.396219E+04, 2.497505E+04, 2.602245E+04, 2.710526E+04, 2.822438E+04,
2.938072E+04, 3.057522E+04, 3.180880E+04, 3.308241E+04, 3.439702E+04, 3.575361E+04,
3.715316E+04, 3.859669E+04, 4.008520E+04, 4.161973E+04, 4.320132E+04, 4.483101E+04,
4.650988E+04, 4.823901E+04, 5.001948E+04, 5.185240E+04, 5.373887E+04, 5.568001E+04,
5.767697E+04, 5.973088E+04, 6.184289E+04, 6.401416E+04, 6.624588E+04, 6.853920E+04,
7.089533E+04, 7.331546E+04, 7.580078E+04, 7.835251E+04, 8.097186E+04, 8.366006E+04,
8.641834E+04, 8.924792E+04, 9.215004E+04, 9.512594E+04, 9.817687E+04, 1.013041E+05,
1.045088E+05, 1.077923E+05, 1.111559E+05, 1.146007E+05, 1.181281E+05, 1.217393E+05,
1.254356E+05, 1.292182E+05, 1.330883E+05, 1.370473E+05, 1.410964E+05, 1.452368E+05,
1.494697E+05, 1.537965E+05, 1.582183E+05, 1.627364E+05, 1.673520E+05, 1.720663E+05,
1.768805E+05, 1.817959E+05, 1.868137E+05, 1.919349E+05, 1.971610E+05, 2.024929E+05,
2.079318E+05, 2.134791E+05, 2.191356E+05, 2.249027E+05, 2.307815E+05, 2.367730E+05,
2.428784E+05, 2.490987E+05, 2.554351E+05, 2.618886E+05, 2.684603E+05, 2.751512E+05,
2.819624E+05, 2.888948E+05, 2.959496E+05, 3.031276E+05, 3.104298E+05, 3.178573E+05,
3.254109E+05, 3.330916E+05, 3.409004E+05, 3.488380E+05, 3.569055E+05, 3.651036E+05,
3.734333E+05, 3.818953E+05, 3.904905E+05, 3.992197E+05, 4.080837E+05, 4.170833E+05,
4.262192E+05, 4.354922E+05,
])
# ---------------------- M = 2, I = 4 ---------------------------
M = 2
I = 4
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.325894E+01, 2.223057E+02, 4.426057E+02, 6.629202E+02, 8.832573E+02, 1.103737E+03,
1.324841E+03, 1.547611E+03, 1.773607E+03, 2.004700E+03, 2.242871E+03, 2.490065E+03,
2.748119E+03, 3.018750E+03, 3.303554E+03, 3.604030E+03, 3.921599E+03, 4.257630E+03,
4.613454E+03, 4.990381E+03, 5.389715E+03, 5.812764E+03, 6.260847E+03, 6.735300E+03,
7.237487E+03, 7.768800E+03, 8.330662E+03, 8.924535E+03, 9.551919E+03, 1.021436E+04,
1.091343E+04, 1.165077E+04, 1.242805E+04, 1.324700E+04, 1.410938E+04, 1.501703E+04,
1.597182E+04, 1.697567E+04, 1.803057E+04, 1.913855E+04, 2.030170E+04, 2.152217E+04,
2.280215E+04, 2.414392E+04, 2.554979E+04, 2.702215E+04, 2.856342E+04, 3.017611E+04,
3.186279E+04, 3.362608E+04, 3.546867E+04, 3.739332E+04, 3.940283E+04, 4.150010E+04,
4.368808E+04, 4.596979E+04, 4.834831E+04, 5.082679E+04, 5.340848E+04, 5.609665E+04,
5.889468E+04, 6.180600E+04, 6.483413E+04, 6.798265E+04, 7.125521E+04, 7.465556E+04,
7.818749E+04, 8.185488E+04, 8.566170E+04, 8.961199E+04, 9.370984E+04, 9.795946E+04,
1.023651E+05, 1.069311E+05, 1.116619E+05, 1.165621E+05, 1.216361E+05, 1.268886E+05,
1.323244E+05, 1.379484E+05, 1.437653E+05, 1.497802E+05, 1.559982E+05, 1.624244E+05,
1.690639E+05, 1.759222E+05, 1.830045E+05, 1.903164E+05, 1.978633E+05, 2.056509E+05,
2.136848E+05, 2.219708E+05, 2.305147E+05, 2.393225E+05, 2.484001E+05, 2.577535E+05,
2.673889E+05, 2.773125E+05, 2.875304E+05, 2.980491E+05, 3.088749E+05, 3.200142E+05,
3.314736E+05, 3.432596E+05, 3.553788E+05, 3.678379E+05, 3.806436E+05, 3.938026E+05,
4.073219E+05, 4.212082E+05, 4.354685E+05, 4.501096E+05, 4.651387E+05, 4.805627E+05,
4.963887E+05, 5.126236E+05, 5.292748E+05, 5.463492E+05, 5.638540E+05, 5.817965E+05,
6.001837E+05, 6.190230E+05, 6.383214E+05, 6.580863E+05, 6.783248E+05, 6.990442E+05,
7.202517E+05, 7.419545E+05, 7.641599E+05, 7.868749E+05, 8.101069E+05, 8.338629E+05,
8.581502E+05, 8.829758E+05, 9.083470E+05, 9.342706E+05, 9.607539E+05, 9.878038E+05,
1.015427E+06, 1.043631E+06, 1.072423E+06, 1.101809E+06, 1.131795E+06, 1.162390E+06,
1.193600E+06, 1.225430E+06, 1.257889E+06, 1.290982E+06, 1.324715E+06, 1.359096E+06,
1.394131E+06, 1.429826E+06, 1.466187E+06, 1.503220E+06, 1.540932E+06, 1.579328E+06,
1.618415E+06, 1.658198E+06, 1.698683E+06, 1.739876E+06, 1.781782E+06, 1.824407E+06,
1.867756E+06, 1.911835E+06, 1.956650E+06, 2.002204E+06, 2.048503E+06, 2.095553E+06,
2.143358E+06, 2.191923E+06, 2.241253E+06, 2.291353E+06, 2.342226E+06, 2.393878E+06,
2.446312E+06, 2.499534E+06,
])
# ---------------------- M = 2, I = 5 ---------------------------
M = 2
I = 5
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
4.521080E+00, 7.618089E+01, 1.516935E+02, 2.272111E+02, 3.027382E+02, 3.783295E+02,
4.541900E+02, 5.307384E+02, 6.085758E+02, 6.884015E+02, 7.709370E+02, 8.568792E+02,
9.468802E+02, 1.041544E+03, 1.141433E+03, 1.247075E+03, 1.358972E+03, 1.477611E+03,
1.603466E+03, 1.737009E+03, 1.878710E+03, 2.029043E+03, 2.188489E+03, 2.357536E+03,
2.536684E+03, 2.726445E+03, 2.927343E+03, 3.139917E+03, 3.364724E+03, 3.602332E+03,
3.853330E+03, 4.118322E+03, 4.397931E+03, 4.692798E+03, 5.003582E+03, 5.330962E+03,
5.675636E+03, 6.038323E+03, 6.419760E+03, 6.820707E+03, 7.241943E+03, 7.684269E+03,
8.148509E+03, 8.635506E+03, 9.146128E+03, 9.681264E+03, 1.024183E+04, 1.082875E+04,
1.144299E+04, 1.208554E+04, 1.275739E+04, 1.345959E+04, 1.419317E+04, 1.495924E+04,
1.575889E+04, 1.659324E+04, 1.746347E+04, 1.837075E+04, 1.931629E+04, 2.030133E+04,
2.132713E+04, 2.239497E+04, 2.350618E+04, 2.466211E+04, 2.586412E+04, 2.711361E+04,
2.841201E+04, 2.976079E+04, 3.116142E+04, 3.261543E+04, 3.412435E+04, 3.568975E+04,
3.731326E+04, 3.899648E+04, 4.074109E+04, 4.254878E+04, 4.442127E+04, 4.636031E+04,
4.836769E+04, 5.044520E+04, 5.259471E+04, 5.481807E+04, 5.711720E+04, 5.949401E+04,
6.195048E+04, 6.448859E+04, 6.711037E+04, 6.981786E+04, 7.261315E+04, 7.549834E+04,
7.847557E+04, 8.154701E+04, 8.471484E+04, 8.798130E+04, 9.134862E+04, 9.481909E+04,
9.839500E+04, 1.020787E+05, 1.058725E+05, 1.097788E+05, 1.138000E+05, 1.179385E+05,
1.221969E+05, 1.265774E+05, 1.310827E+05, 1.357152E+05, 1.404776E+05, 1.453722E+05,
1.504017E+05, 1.555688E+05, 1.608759E+05, 1.663257E+05, 1.719208E+05, 1.776640E+05,
1.835577E+05, 1.896048E+05, 1.958079E+05, 2.021696E+05, 2.086927E+05, 2.153799E+05,
2.222339E+05, 2.292574E+05, 2.364532E+05, 2.438239E+05, 2.513722E+05, 2.591010E+05,
2.670130E+05, 2.751108E+05, 2.833972E+05, 2.918749E+05, 3.005467E+05, 3.094152E+05,
3.184831E+05, 3.277532E+05, 3.372281E+05, 3.469105E+05, 3.568031E+05, 3.669086E+05,
3.772295E+05, 3.877685E+05, 3.985282E+05, 4.095113E+05, 4.207202E+05, 4.321576E+05,
4.438260E+05, 4.557279E+05, 4.678659E+05, 4.802424E+05, 4.928599E+05, 5.057207E+05,
5.188275E+05, 5.321824E+05, 5.457879E+05, 5.596464E+05, 5.737601E+05, 5.881313E+05,
6.027624E+05, 6.176554E+05, 6.328126E+05, 6.482363E+05, 6.639284E+05, 6.798912E+05,
6.961267E+05, 7.126369E+05, 7.294239E+05, 7.464896E+05, 7.638361E+05, 7.814651E+05,
7.993786E+05, 8.175784E+05, 8.360663E+05, 8.548442E+05, 8.739137E+05, 8.932765E+05,
9.129343E+05, 9.328888E+05,
])
# ---------------------- M = 2, I = 6 ---------------------------
M = 2
I = 6
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
2.651606E+01, 4.445925E+02, 8.851739E+02, 1.325785E+03, 1.766449E+03, 2.207477E+03,
2.650035E+03, 3.096522E+03, 3.550398E+03, 4.015699E+03, 4.496597E+03, 4.997122E+03,
5.521046E+03, 6.071862E+03, 6.652812E+03, 7.266940E+03, 7.917143E+03, 8.606215E+03,
9.336891E+03, 1.011188E+04, 1.093387E+04, 1.180560E+04, 1.272982E+04, 1.370933E+04,
1.474700E+04, 1.584577E+04, 1.700865E+04, 1.823873E+04, 1.953919E+04, 2.091330E+04,
2.236443E+04, 2.389604E+04, 2.551171E+04, 2.721511E+04, 2.901001E+04, 3.090030E+04,
3.288998E+04, 3.498317E+04, 3.718410E+04, 3.949710E+04, 4.192665E+04, 4.447733E+04,
4.715386E+04, 4.996106E+04, 5.290390E+04, 5.598748E+04, 5.921700E+04, 6.259783E+04,
6.613544E+04, 6.983547E+04, 7.370367E+04, 7.774594E+04, 8.196832E+04, 8.637699E+04,
9.097828E+04, 9.577865E+04, 1.007847E+05, 1.060033E+05, 1.114413E+05, 1.171057E+05,
1.230038E+05, 1.291430E+05, 1.355307E+05, 1.421748E+05, 1.490831E+05, 1.562635E+05,
1.637242E+05, 1.714737E+05, 1.795203E+05, 1.878728E+05, 1.965400E+05, 2.055309E+05,
2.148547E+05, 2.245208E+05, 2.345385E+05, 2.449177E+05, 2.556682E+05, 2.668000E+05,
2.783233E+05, 2.902486E+05, 3.025862E+05, 3.153471E+05, 3.285420E+05, 3.421821E+05,
3.562785E+05, 3.708427E+05, 3.858862E+05, 4.014209E+05, 4.174586E+05, 4.340114E+05,
4.510916E+05, 4.687115E+05, 4.868838E+05, 5.056212E+05, 5.249365E+05, 5.448429E+05,
5.653535E+05, 5.864817E+05, 6.082409E+05, 6.306448E+05, 6.537072E+05, 6.774420E+05,
7.018632E+05, 7.269850E+05, 7.528217E+05, 7.793877E+05, 8.066975E+05, 8.347658E+05,
8.636073E+05, 8.932368E+05, 9.236693E+05, 9.549198E+05, 9.870035E+05, 1.019935E+06,
1.053731E+06, 1.088405E+06, 1.123974E+06, 1.160452E+06, 1.197856E+06, 1.236200E+06,
1.275501E+06, 1.315774E+06, 1.357034E+06, 1.399297E+06, 1.442580E+06, 1.486897E+06,
1.532264E+06, 1.578697E+06, 1.626211E+06, 1.674823E+06, 1.724548E+06, 1.775400E+06,
1.827397E+06, 1.880553E+06, 1.934884E+06, 1.990405E+06, 2.047132E+06, 2.105079E+06,
2.164262E+06, 2.224697E+06, 2.286397E+06, 2.349379E+06, 2.413656E+06, 2.479244E+06,
2.546157E+06, 2.614410E+06, 2.684016E+06, 2.754992E+06, 2.827349E+06, 2.901104E+06,
2.976268E+06, 3.052857E+06, 3.130883E+06, 3.210361E+06, 3.291303E+06, 3.373723E+06,
3.457633E+06, 3.543047E+06, 3.629976E+06, 3.718434E+06, 3.808433E+06, 3.899985E+06,
3.993102E+06, 4.087795E+06, 4.184076E+06, 4.281956E+06, 4.381448E+06, 4.482561E+06,
4.585306E+06, 4.689695E+06, 4.795737E+06, 4.903442E+06, 5.012821E+06, 5.123884E+06,
5.236639E+06, 5.351097E+06,
])
# ---------------------- M = 2, I = 7 ---------------------------
M = 2
I = 7
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[0]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.250890E+00, 2.020899E+01, 4.025104E+01, 6.029436E+01, 8.033986E+01, 1.003996E+02,
1.205209E+02, 1.408040E+02, 1.613971E+02, 1.824767E+02, 2.042283E+02, 2.268340E+02,
2.504658E+02, 2.752848E+02, 3.014409E+02, 3.290757E+02, 3.583237E+02, 3.893145E+02,
4.221748E+02, 4.570293E+02, 4.940023E+02, 5.332187E+02, 5.748044E+02, 6.188873E+02,
6.655979E+02, 7.150694E+02, 7.674382E+02, 8.228445E+02, 8.814321E+02, 9.433489E+02,
1.008747E+03, 1.077783E+03, 1.150618E+03, 1.227417E+03, 1.308352E+03, 1.393596E+03,
1.483332E+03, 1.577744E+03, 1.677024E+03, 1.781366E+03, 1.890973E+03, 2.006051E+03,
2.126813E+03, 2.253477E+03, 2.386266E+03, 2.525410E+03, 2.671145E+03, 2.823711E+03,
2.983356E+03, 3.150333E+03, 3.324902E+03, 3.507329E+03, 3.697886E+03, 3.896851E+03,
4.104509E+03, 4.321152E+03, 4.547077E+03, 4.782590E+03, 5.028001E+03, 5.283630E+03,
5.549800E+03, 5.826843E+03, 6.115099E+03, 6.414913E+03, 6.726639E+03, 7.050636E+03,
7.387272E+03, 7.736920E+03, 8.099964E+03, 8.476792E+03, 8.867801E+03, 9.273394E+03,
9.693984E+03, 1.012999E+04, 1.058184E+04, 1.104996E+04, 1.153481E+04, 1.203682E+04,
1.255646E+04, 1.309419E+04, 1.365049E+04, 1.422584E+04, 1.482072E+04, 1.543563E+04,
1.607109E+04, 1.672760E+04, 1.740569E+04, 1.810588E+04, 1.882871E+04, 1.957473E+04,
2.034450E+04, 2.113858E+04, 2.195754E+04, 2.280195E+04, 2.367242E+04, 2.456953E+04,
2.549389E+04, 2.644612E+04, 2.742683E+04, 2.843666E+04, 2.947625E+04, 3.054624E+04,
3.164730E+04, 3.278009E+04, 3.394529E+04, 3.514357E+04, 3.637564E+04, 3.764218E+04,
3.894392E+04, 4.028157E+04, 4.165585E+04, 4.306751E+04, 4.451729E+04, 4.600595E+04,
4.753424E+04, 4.910294E+04, 5.071284E+04, 5.236471E+04, 5.405937E+04, 5.579761E+04,
5.758027E+04, 5.940815E+04, 6.128210E+04, 6.320296E+04, 6.517158E+04, 6.718883E+04,
6.925557E+04, 7.137269E+04, 7.354107E+04, 7.576161E+04, 7.803522E+04, 8.036280E+04,
8.274529E+04, 8.518362E+04, 8.767872E+04, 9.023154E+04, 9.284305E+04, 9.551420E+04,
9.824598E+04, 1.010394E+05, 1.038953E+05, 1.068149E+05, 1.097991E+05, 1.128489E+05,
1.159653E+05, 1.191494E+05, 1.224023E+05, 1.257249E+05, 1.291182E+05, 1.325835E+05,
1.361217E+05, 1.397340E+05, 1.434213E+05, 1.471849E+05, 1.510257E+05, 1.549449E+05,
1.589437E+05, 1.630231E+05, 1.671842E+05, 1.714282E+05, 1.757562E+05, 1.801694E+05,
1.846689E+05, 1.892559E+05, 1.939315E+05, 1.986969E+05, 2.035533E+05, 2.085017E+05,
2.135436E+05, 2.186799E+05, 2.239119E+05, 2.292408E+05, 2.346678E+05, 2.401941E+05,
2.458208E+05, 2.515493E+05, 2.573807E+05, 2.633163E+05, 2.693572E+05, 2.755047E+05,
2.817600E+05, 2.881243E+05, 2.945989E+05, 3.011850E+05, 3.078838E+05, 3.146966E+05,
3.216246E+05, 3.286691E+05, 3.358312E+05, 3.431123E+05, 3.505135E+05, 3.580362E+05,
3.656815E+05, 3.734508E+05, 3.813451E+05, 3.893659E+05, 3.975143E+05, 4.057916E+05,
4.141989E+05, 4.227377E+05, 4.314090E+05, 4.402142E+05, 4.491544E+05, 4.582309E+05,
4.674450E+05, 4.767978E+05, 4.862906E+05, 4.959246E+05, 5.057011E+05, 5.156212E+05,
5.256861E+05, 5.358972E+05, 5.462555E+05, 5.567623E+05, 5.674188E+05, 5.782262E+05,
5.891856E+05, 6.002984E+05, 6.115655E+05, 6.229883E+05, 6.345679E+05, 6.463054E+05,
6.582021E+05, 6.702590E+05, 6.824774E+05, 6.948584E+05, 7.074030E+05, 7.201125E+05,
7.329880E+05, 7.460305E+05, 7.592412E+05, 7.726213E+05, 7.861717E+05, 7.998937E+05,
8.137882E+05, 8.278563E+05, 8.420992E+05, 8.565179E+05, 8.711134E+05, 8.858868E+05,
9.008391E+05, 9.159714E+05, 9.312846E+05, 9.467798E+05, 9.624580E+05, 9.783202E+05,
9.943673E+05, 1.010600E+06, 1.027020E+06, 1.043628E+06, 1.060425E+06,
])
# ---------------------- M = 2, I = 8 ---------------------------
M = 2
I = 8
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.391189E+01, 2.356850E+02, 4.693664E+02, 7.030628E+02, 9.367842E+02, 1.170667E+03,
1.405248E+03, 1.641677E+03, 1.881655E+03, 2.127217E+03, 2.380506E+03, 2.643624E+03,
2.918557E+03, 3.207165E+03, 3.511177E+03, 3.832224E+03, 4.171854E+03, 4.531559E+03,
4.912793E+03, 5.316991E+03, 5.745580E+03, 6.199992E+03, 6.681674E+03, 7.192093E+03,
7.732745E+03, 8.305157E+03, 8.910895E+03, 9.551564E+03, 1.022881E+04, 1.094434E+04,
1.169988E+04, 1.249723E+04, 1.333824E+04, 1.422480E+04, 1.515886E+04, 1.614244E+04,
1.717760E+04, 1.826646E+04, 1.941121E+04, 2.061408E+04, 2.187739E+04, 2.320350E+04,
2.459484E+04, 2.605391E+04, 2.758326E+04, 2.918551E+04, 3.086338E+04, 3.261960E+04,
3.445702E+04, 3.637852E+04, 3.838709E+04, 4.048575E+04, 4.267762E+04, 4.496589E+04,
4.735382E+04, 4.984473E+04, 5.244204E+04, 5.514923E+04, 5.796986E+04, 6.090757E+04,
6.396608E+04, 6.714918E+04, 7.046075E+04, 7.390475E+04, 7.748521E+04, 8.120625E+04,
8.507207E+04, 8.908696E+04, 9.325528E+04, 9.758148E+04, 1.020701E+05, 1.067258E+05,
1.115532E+05, 1.165571E+05, 1.217425E+05, 1.271142E+05, 1.326774E+05, 1.384371E+05,
1.443986E+05, 1.505672E+05, 1.569483E+05, 1.635474E+05, 1.703700E+05, 1.774218E+05,
1.847086E+05, 1.922361E+05, 2.000103E+05, 2.080372E+05, 2.163229E+05, 2.248735E+05,
2.336952E+05, 2.427945E+05, 2.521776E+05, 2.618512E+05, 2.718217E+05, 2.820959E+05,
2.926803E+05, 3.035819E+05, 3.148074E+05, 3.263639E+05, 3.382582E+05, 3.504976E+05,
3.630890E+05, 3.760398E+05, 3.893571E+05, 4.030483E+05, 4.171208E+05, 4.315819E+05,
4.464392E+05, 4.617002E+05, 4.773724E+05, 4.934635E+05, 5.099811E+05, 5.269329E+05,
5.443266E+05, 5.621700E+05, 5.804710E+05, 5.992372E+05, 6.184765E+05, 6.381968E+05,
6.584059E+05, 6.791119E+05, 7.003224E+05, 7.220455E+05, 7.442890E+05, 7.670610E+05,
7.903691E+05, 8.142215E+05, 8.386259E+05, 8.635903E+05, 8.891225E+05, 9.152303E+05,
9.419216E+05, 9.692043E+05, 9.970860E+05, 1.025575E+06, 1.054678E+06, 1.084403E+06,
1.114758E+06, 1.145751E+06, 1.177388E+06, 1.209678E+06, 1.242628E+06, 1.276246E+06,
1.310538E+06, 1.345512E+06, 1.381175E+06, 1.417535E+06, 1.454598E+06, 1.492372E+06,
1.530863E+06, 1.570079E+06, 1.610026E+06, 1.650710E+06, 1.692140E+06, 1.734320E+06,
1.777258E+06, 1.820960E+06, 1.865433E+06, 1.910682E+06, 1.956714E+06, 2.003535E+06,
2.051150E+06, 2.099566E+06, 2.148789E+06, 2.198824E+06, 2.249676E+06, 2.301352E+06,
2.353856E+06, 2.407194E+06, 2.461371E+06, 2.516392E+06, 2.572262E+06, 2.628986E+06,
2.686569E+06, 2.745015E+06,
])
# ---------------------- M = 2, I = 9 ---------------------------
M = 2
I = 9
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[0]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
4.124397E+01, 6.874652E+02, 1.368916E+03, 2.050410E+03, 2.731976E+03, 3.413998E+03,
4.098007E+03, 4.787289E+03, 5.486743E+03, 6.202233E+03, 6.939947E+03, 7.705959E+03,
8.506016E+03, 9.345477E+03, 1.022934E+04, 1.116230E+04, 1.214882E+04, 1.319320E+04,
1.429960E+04, 1.547216E+04, 1.671497E+04, 1.803216E+04, 1.942786E+04, 2.090629E+04,
2.247174E+04, 2.412860E+04, 2.588134E+04, 2.773459E+04, 2.969305E+04, 3.176160E+04,
3.394522E+04, 3.624906E+04, 3.867840E+04, 4.123867E+04, 4.393547E+04, 4.677456E+04,
4.976184E+04, 5.290340E+04, 5.620549E+04, 5.967453E+04, 6.331712E+04, 6.714003E+04,
7.115023E+04, 7.535485E+04, 7.976121E+04, 8.437683E+04, 8.920942E+04, 9.426687E+04,
9.955728E+04, 1.050889E+05, 1.108703E+05, 1.169102E+05, 1.232174E+05, 1.298010E+05,
1.366705E+05, 1.438352E+05, 1.513051E+05, 1.590899E+05, 1.672000E+05, 1.756458E+05,
1.844378E+05, 1.935870E+05, 2.031044E+05, 2.130014E+05, 2.232894E+05, 2.339804E+05,
2.450863E+05, 2.566195E+05, 2.685923E+05, 2.810176E+05, 2.939083E+05, 3.072777E+05,
3.211394E+05, 3.355069E+05, 3.503944E+05, 3.658161E+05, 3.817865E+05, 3.983203E+05,
4.154326E+05, 4.331386E+05, 4.514539E+05, 4.703944E+05, 4.899760E+05, 5.102151E+05,
5.311284E+05, 5.527328E+05, 5.750453E+05, 5.980834E+05, 6.218648E+05, 6.464075E+05,
6.717298E+05, 6.978501E+05, 7.247874E+05, 7.525606E+05, 7.811893E+05, 8.106929E+05,
8.410916E+05, 8.724056E+05, 9.046553E+05, 9.378615E+05, 9.720455E+05, 1.007229E+06,
1.043432E+06, 1.080679E+06, 1.118991E+06, 1.158390E+06, 1.198899E+06, 1.240543E+06,
1.283343E+06, 1.327324E+06, 1.372511E+06, 1.418926E+06, 1.466595E+06, 1.515544E+06,
1.565796E+06, 1.617378E+06, 1.670316E+06, 1.724635E+06, 1.780363E+06, 1.837526E+06,
1.896150E+06, 1.956265E+06, 2.017896E+06, 2.081072E+06, 2.145822E+06, 2.212173E+06,
2.280156E+06, 2.349798E+06, 2.421130E+06, 2.494182E+06, 2.568982E+06, 2.645562E+06,
2.723953E+06, 2.804185E+06, 2.886290E+06, 2.970298E+06, 3.056243E+06, 3.144156E+06,
3.234069E+06, 3.326015E+06, 3.420028E+06, 3.516140E+06, 3.614384E+06, 3.714796E+06,
3.817408E+06, 3.922256E+06, 4.029373E+06, 4.138795E+06, 4.250556E+06, 4.364693E+06,
4.481240E+06, 4.600234E+06, 4.721710E+06, 4.845706E+06, 4.972258E+06, 5.101402E+06,
5.233175E+06, 5.367616E+06, 5.504761E+06, 5.644648E+06, 5.787315E+06, 5.932800E+06,
6.081142E+06, 6.232379E+06, 6.386550E+06, 6.543694E+06, 6.703849E+06, 6.867056E+06,
7.033353E+06, 7.202781E+06, 7.375378E+06, 7.551186E+06, 7.730243E+06, 7.912590E+06,
8.098268E+06, 8.287317E+06, 8.479777E+06, 8.675690E+06, 8.875095E+06, 9.078035E+06,
9.284549E+06, 9.494679E+06, 9.708467E+06, 9.925953E+06, 1.014718E+07, 1.037219E+07,
1.060102E+07, 1.083371E+07, 1.107031E+07, 1.131086E+07, 1.155539E+07, 1.180396E+07,
1.205660E+07, 1.231335E+07, 1.257426E+07, 1.283936E+07, 1.310870E+07, 1.338233E+07,
1.366027E+07, 1.394258E+07, 1.422929E+07, 1.452045E+07, 1.481610E+07, 1.511628E+07,
1.542103E+07, 1.573039E+07, 1.604440E+07, 1.636310E+07, 1.668655E+07, 1.701476E+07,
1.734779E+07, 1.768569E+07, 1.802847E+07, 1.837620E+07, 1.872890E+07, 1.908663E+07,
1.944941E+07, 1.981729E+07, 2.019030E+07, 2.056850E+07, 2.095191E+07, 2.134058E+07,
2.173454E+07, 2.213383E+07, 2.253850E+07, 2.294858E+07, 2.336411E+07, 2.378512E+07,
2.421166E+07, 2.464377E+07, 2.508147E+07, 2.552481E+07, 2.597382E+07, 2.642854E+07,
2.688901E+07, 2.735527E+07, 2.782734E+07, 2.830527E+07, 2.878909E+07, 2.927883E+07,
2.977453E+07, 3.027623E+07, 3.078396E+07, 3.129774E+07, 3.181763E+07, 3.234365E+07,
3.287583E+07, 3.341421E+07, 3.395881E+07, 3.450968E+07, 3.506684E+07,
])
# ---------------------- M = 2, I = 10 ---------------------------
M = 2
I = 10
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[0]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
2.501700E+00, 4.041606E+01, 8.049824E+01, 1.205830E+02, 1.606730E+02, 2.007995E+02,
2.410771E+02, 2.817371E+02, 3.231082E+02, 3.655703E+02, 4.095143E+02, 4.553170E+02,
5.033312E+02, 5.538845E+02, 6.072820E+02, 6.638110E+02, 7.237459E+02, 7.873522E+02,
8.548902E+02, 9.266178E+02, 1.002793E+03, 1.083677E+03, 1.169533E+03, 1.260630E+03,
1.357243E+03, 1.459653E+03, 1.568150E+03, 1.683030E+03, 1.804598E+03, 1.933171E+03,
2.069070E+03, 2.212630E+03, 2.364195E+03, 2.524117E+03, 2.692761E+03, 2.870501E+03,
3.057723E+03, 3.254823E+03, 3.462209E+03, 3.680301E+03, 3.909530E+03, 4.150338E+03,
4.403179E+03, 4.668522E+03, 4.946845E+03, 5.238639E+03, 5.544410E+03, 5.864675E+03,
6.199964E+03, 6.550821E+03, 6.917802E+03, 7.301478E+03, 7.702433E+03, 8.121265E+03,
8.558585E+03, 9.015020E+03, 9.491209E+03, 9.987807E+03, 1.050548E+04, 1.104492E+04,
1.160682E+04, 1.219189E+04, 1.280087E+04, 1.343449E+04, 1.409352E+04, 1.477873E+04,
1.549091E+04, 1.623087E+04, 1.699943E+04, 1.779742E+04, 1.862571E+04, 1.948515E+04,
2.037665E+04, 2.130110E+04, 2.225942E+04, 2.325254E+04, 2.428143E+04, 2.534705E+04,
2.645038E+04, 2.759244E+04, 2.877424E+04, 2.999682E+04, 3.126123E+04, 3.256856E+04,
3.391989E+04, 3.531632E+04, 3.675900E+04, 3.824905E+04, 3.978765E+04, 4.137597E+04,
4.301520E+04, 4.470658E+04, 4.645132E+04, 4.825069E+04, 5.010596E+04, 5.201841E+04,
5.398935E+04, 5.602011E+04, 5.811204E+04, 6.026650E+04, 6.248487E+04, 6.476855E+04,
6.711897E+04, 6.953757E+04, 7.202580E+04, 7.458515E+04, 7.721711E+04, 7.992319E+04,
8.270494E+04, 8.556390E+04, 8.850166E+04, 9.151980E+04, 9.461994E+04, 9.780372E+04,
1.010728E+05, 1.044288E+05, 1.078734E+05, 1.114085E+05, 1.150356E+05, 1.187565E+05,
1.225730E+05, 1.264870E+05, 1.305001E+05, 1.346143E+05, 1.388313E+05, 1.431531E+05,
1.475815E+05, 1.521185E+05, 1.567659E+05, 1.615257E+05, 1.663998E+05, 1.713903E+05,
1.764992E+05, 1.817284E+05, 1.870800E+05, 1.925560E+05, 1.981586E+05, 2.038898E+05,
2.097518E+05, 2.157466E+05, 2.218764E+05, 2.281434E+05, 2.345498E+05, 2.410978E+05,
2.477895E+05, 2.546272E+05, 2.616132E+05, 2.687498E+05, 2.760392E+05, 2.834837E+05,
2.910857E+05, 2.988474E+05, 3.067713E+05, 3.148597E+05, 3.231150E+05, 3.315396E+05,
3.401358E+05, 3.489062E+05, 3.578531E+05, 3.669790E+05, 3.762864E+05, 3.857777E+05,
3.954555E+05, 4.053222E+05, 4.153804E+05, 4.256326E+05, 4.360813E+05, 4.467291E+05,
4.575786E+05, 4.686322E+05, 4.798927E+05, 4.913626E+05, 5.030444E+05, 5.149409E+05,
5.270546E+05, 5.393881E+05, 5.519441E+05, 5.647252E+05, 5.777342E+05, 5.909735E+05,
6.044459E+05, 6.181541E+05, 6.321007E+05, 6.462885E+05, 6.607200E+05, 6.753979E+05,
6.903251E+05, 7.055041E+05, 7.209376E+05, 7.366284E+05, 7.525791E+05, 7.687924E+05,
7.852711E+05, 8.020178E+05, 8.190353E+05, 8.363262E+05, 8.538932E+05, 8.717391E+05,
8.898665E+05, 9.082781E+05, 9.269767E+05, 9.459648E+05, 9.652453E+05, 9.848207E+05,
1.004694E+06, 1.024867E+06, 1.045343E+06, 1.066125E+06, 1.087216E+06, 1.108617E+06,
1.130332E+06, 1.152363E+06, 1.174712E+06, 1.197384E+06, 1.220379E+06, 1.243701E+06,
1.267352E+06, 1.291335E+06, 1.315653E+06, 1.340307E+06, 1.365301E+06, 1.390637E+06,
1.416318E+06, 1.442345E+06, 1.468723E+06, 1.495452E+06, 1.522536E+06, 1.549977E+06,
1.577777E+06, 1.605939E+06, 1.634466E+06, 1.663359E+06, 1.692621E+06, 1.722255E+06,
1.752263E+06, 1.782647E+06, 1.813409E+06, 1.844552E+06, 1.876079E+06, 1.907990E+06,
1.940290E+06, 1.972979E+06, 2.006060E+06, 2.039536E+06, 2.073408E+06, 2.107678E+06,
2.142350E+06, 2.177424E+06, 2.212904E+06, 2.248791E+06, 2.285086E+06,
])
# --------------- CO2 838: M = 2, I = 0 ALIAS-----------------
TIPS_2017_ISOT_HASH[(M,0)] = TIPS_2017_ISOT[0]
TIPS_2017_ISOQ_HASH[(M,0)] = TIPS_2017_ISOQ_HASH[(M,I)]
# ---------------------- M = 2, I = 11 ---------------------------
M = 2
I = 11
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
2.782335E+01, 4.713557E+02, 9.387029E+02, 1.406080E+03, 1.873518E+03, 2.341368E+03,
2.810932E+03, 3.284859E+03, 3.766925E+03, 4.261513E+03, 4.773136E+03, 5.306147E+03,
5.864623E+03, 6.452344E+03, 7.072828E+03, 7.729387E+03, 8.425175E+03, 9.163245E+03,
9.946589E+03, 1.077817E+04, 1.166095E+04, 1.259791E+04, 1.359209E+04, 1.464656E+04,
1.576448E+04, 1.694906E+04, 1.820363E+04, 1.953159E+04, 2.093643E+04, 2.242176E+04,
2.399128E+04, 2.564882E+04, 2.739831E+04, 2.924378E+04, 3.118942E+04, 3.323950E+04,
3.539843E+04, 3.767076E+04, 4.006115E+04, 4.257440E+04, 4.521543E+04, 4.798931E+04,
5.090124E+04, 5.395655E+04, 5.716074E+04, 6.051942E+04, 6.403836E+04, 6.772347E+04,
7.158083E+04, 7.561663E+04, 7.983725E+04, 8.424921E+04, 8.885918E+04, 9.367400E+04,
9.870066E+04, 1.039463E+05, 1.094183E+05, 1.151240E+05, 1.210712E+05, 1.272677E+05,
1.337214E+05, 1.404405E+05, 1.474333E+05, 1.547084E+05, 1.622744E+05, 1.701402E+05,
1.783147E+05, 1.868073E+05, 1.956274E+05, 2.047844E+05, 2.142882E+05, 2.241486E+05,
2.343760E+05, 2.449804E+05, 2.559725E+05, 2.673630E+05, 2.791627E+05, 2.913827E+05,
3.040342E+05, 3.171288E+05, 3.306779E+05, 3.446935E+05, 3.591876E+05, 3.741723E+05,
3.896601E+05, 4.056634E+05, 4.221951E+05, 4.392682E+05, 4.568957E+05, 4.750909E+05,
4.938673E+05, 5.132387E+05, 5.332187E+05, 5.538215E+05, 5.750612E+05, 5.969522E+05,
6.195090E+05, 6.427462E+05, 6.666788E+05, 6.913216E+05, 7.166898E+05, 7.427988E+05,
7.696639E+05, 7.973007E+05, 8.257250E+05, 8.549525E+05, 8.849992E+05, 9.158811E+05,
9.476145E+05, 9.802157E+05, 1.013701E+06, 1.048087E+06, 1.083390E+06, 1.119627E+06,
1.156815E+06, 1.194970E+06, 1.234110E+06, 1.274250E+06, 1.315409E+06, 1.357604E+06,
1.400851E+06, 1.445167E+06, 1.490570E+06, 1.537077E+06, 1.584704E+06, 1.633470E+06,
1.683392E+06, 1.734485E+06, 1.786769E+06, 1.840259E+06, 1.894973E+06, 1.950928E+06,
2.008141E+06, 2.066630E+06, 2.126410E+06, 2.187498E+06, 2.249913E+06, 2.313669E+06,
2.378785E+06, 2.445276E+06, 2.513159E+06, 2.582450E+06, 2.653165E+06, 2.725322E+06,
2.798935E+06, 2.874020E+06, 2.950594E+06, 3.028672E+06, 3.108269E+06, 3.189401E+06,
3.272083E+06, 3.356330E+06, 3.442157E+06, 3.529579E+06, 3.618610E+06, 3.709264E+06,
3.801557E+06, 3.895501E+06, 3.991111E+06, 4.088400E+06, 4.187382E+06, 4.288070E+06,
4.390478E+06, 4.494617E+06, 4.600501E+06, 4.708142E+06, 4.817553E+06, 4.928745E+06,
5.041730E+06, 5.156520E+06, 5.273126E+06, 5.391560E+06, 5.511832E+06, 5.633953E+06,
5.757934E+06, 5.883784E+06,
])
# ---------------------- M = 2, I = 12 ---------------------------
M = 2
I = 12
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[0]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
8.248682E+01, 1.374885E+03, 2.737741E+03, 4.100686E+03, 5.463802E+03, 6.828082E+03,
8.197234E+03, 9.578835E+03, 1.098375E+04, 1.242462E+04, 1.391448E+04, 1.546591E+04,
1.709070E+04, 1.879977E+04, 2.060326E+04, 2.251071E+04, 2.453122E+04, 2.667355E+04,
2.894631E+04, 3.135799E+04, 3.391712E+04, 3.663224E+04, 3.951206E+04, 4.256541E+04,
4.580131E+04, 4.922903E+04, 5.285804E+04, 5.669811E+04, 6.075926E+04, 6.505180E+04,
6.958637E+04, 7.437390E+04, 7.942564E+04, 8.475318E+04, 9.036846E+04, 9.628373E+04,
1.025116E+05, 1.090652E+05, 1.159576E+05, 1.232028E+05, 1.308148E+05, 1.388081E+05,
1.471975E+05, 1.559983E+05, 1.652263E+05, 1.748975E+05, 1.850284E+05, 1.956358E+05,
2.067372E+05, 2.183504E+05, 2.304934E+05, 2.431851E+05, 2.564443E+05, 2.702908E+05,
2.847445E+05, 2.998258E+05, 3.155556E+05, 3.319555E+05, 3.490472E+05, 3.668531E+05,
3.853960E+05, 4.046992E+05, 4.247867E+05, 4.456827E+05, 4.674120E+05, 4.900000E+05,
5.134725E+05, 5.378560E+05, 5.631772E+05, 5.894637E+05, 6.167433E+05, 6.450447E+05,
6.743967E+05, 7.048290E+05, 7.363717E+05, 7.690554E+05, 8.029115E+05, 8.379716E+05,
8.742681E+05, 9.118340E+05, 9.507027E+05, 9.909083E+05, 1.032485E+06, 1.075469E+06,
1.119896E+06, 1.165801E+06, 1.213222E+06, 1.262197E+06, 1.312763E+06, 1.364960E+06,
1.418826E+06, 1.474402E+06, 1.531729E+06, 1.590847E+06, 1.651799E+06, 1.714626E+06,
1.779372E+06, 1.846081E+06, 1.914797E+06, 1.985564E+06, 2.058429E+06, 2.133437E+06,
2.210636E+06, 2.290073E+06, 2.371795E+06, 2.455852E+06, 2.542294E+06, 2.631169E+06,
2.722529E+06, 2.816425E+06, 2.912910E+06, 3.012035E+06, 3.113855E+06, 3.218422E+06,
3.325792E+06, 3.436020E+06, 3.549161E+06, 3.665273E+06, 3.784412E+06, 3.906637E+06,
4.032006E+06, 4.160578E+06, 4.292413E+06, 4.427572E+06, 4.566115E+06, 4.708105E+06,
4.853604E+06, 5.002674E+06, 5.155381E+06, 5.311788E+06, 5.471961E+06, 5.635964E+06,
5.803865E+06, 5.975731E+06, 6.151628E+06, 6.331626E+06, 6.515793E+06, 6.704199E+06,
6.896914E+06, 7.094009E+06, 7.295554E+06, 7.501623E+06, 7.712287E+06, 7.927620E+06,
8.147695E+06, 8.372588E+06, 8.602372E+06, 8.837123E+06, 9.076918E+06, 9.321833E+06,
9.571945E+06, 9.827332E+06, 1.008807E+07, 1.035424E+07, 1.062593E+07, 1.090320E+07,
1.118615E+07, 1.147484E+07, 1.176937E+07, 1.206982E+07, 1.237626E+07, 1.268879E+07,
1.300747E+07, 1.333241E+07, 1.366367E+07, 1.400135E+07, 1.434553E+07, 1.469629E+07,
1.505372E+07, 1.541791E+07, 1.578894E+07, 1.616690E+07, 1.655188E+07, 1.694395E+07,
1.734322E+07, 1.774977E+07, 1.816368E+07, 1.858505E+07, 1.901396E+07, 1.945050E+07,
1.989476E+07, 2.034683E+07, 2.080679E+07, 2.127475E+07, 2.175079E+07, 2.223499E+07,
2.272745E+07, 2.322825E+07, 2.373750E+07, 2.425527E+07, 2.478166E+07, 2.531676E+07,
2.586066E+07, 2.641344E+07, 2.697521E+07, 2.754605E+07, 2.812604E+07, 2.871529E+07,
2.931387E+07, 2.992189E+07, 3.053943E+07, 3.116658E+07, 3.180343E+07, 3.245007E+07,
3.310659E+07, 3.377308E+07, 3.444963E+07, 3.513633E+07, 3.583326E+07, 3.654053E+07,
3.725820E+07, 3.798638E+07, 3.872515E+07, 3.947459E+07, 4.023480E+07, 4.100587E+07,
4.178787E+07, 4.258090E+07, 4.338505E+07, 4.420039E+07, 4.502701E+07, 4.586501E+07,
4.671445E+07, 4.757544E+07, 4.844805E+07, 4.933236E+07, 5.022846E+07, 5.113643E+07,
5.205636E+07, 5.298832E+07, 5.393239E+07, 5.488867E+07, 5.585722E+07, 5.683812E+07,
5.783147E+07, 5.883732E+07, 5.985577E+07, 6.088689E+07, 6.193076E+07, 6.298745E+07,
6.405704E+07, 6.513961E+07, 6.623523E+07, 6.734397E+07, 6.846590E+07, 6.960111E+07,
7.074966E+07, 7.191162E+07, 7.308707E+07, 7.427607E+07, 7.547870E+07,
])
# ---------------------- M = 2, I = 13 ---------------------------
M = 2
I = 13
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[0]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.172250E+00, 1.797782E+01, 3.578844E+01, 5.360026E+01, 7.141461E+01, 8.924655E+01,
1.071502E+02, 1.252330E+02, 1.436455E+02, 1.625593E+02, 1.821490E+02, 2.025809E+02,
2.240094E+02, 2.465762E+02, 2.704128E+02, 2.956425E+02, 3.223827E+02, 3.507470E+02,
3.808470E+02, 4.127935E+02, 4.466974E+02, 4.826712E+02, 5.208288E+02, 5.612866E+02,
6.041637E+02, 6.495824E+02, 6.976684E+02, 7.485507E+02, 8.023624E+02, 8.592405E+02,
9.193259E+02, 9.827640E+02, 1.049704E+03, 1.120301E+03, 1.194713E+03, 1.273103E+03,
1.355638E+03, 1.442493E+03, 1.533844E+03, 1.629875E+03, 1.730772E+03, 1.836729E+03,
1.947943E+03, 2.064618E+03, 2.186963E+03, 2.315191E+03, 2.449522E+03, 2.590182E+03,
2.737401E+03, 2.891416E+03, 3.052469E+03, 3.220808E+03, 3.396688E+03, 3.580369E+03,
3.772118E+03, 3.972206E+03, 4.180913E+03, 4.398524E+03, 4.625331E+03, 4.861631E+03,
5.107729E+03, 5.363936E+03, 5.630570E+03, 5.907955E+03, 6.196423E+03, 6.496312E+03,
6.807968E+03, 7.131742E+03, 7.467993E+03, 7.817088E+03, 8.179401E+03, 8.555312E+03,
8.945209E+03, 9.349488E+03, 9.768552E+03, 1.020281E+04, 1.065268E+04, 1.111859E+04,
1.160098E+04, 1.210027E+04, 1.261693E+04, 1.315140E+04, 1.370416E+04, 1.427567E+04,
1.486641E+04, 1.547688E+04, 1.610757E+04, 1.675898E+04, 1.743163E+04, 1.812603E+04,
1.884273E+04, 1.958224E+04, 2.034513E+04, 2.113194E+04, 2.194324E+04, 2.277960E+04,
2.364159E+04, 2.452981E+04, 2.544485E+04, 2.638733E+04, 2.735784E+04, 2.835702E+04,
2.938550E+04, 3.044392E+04, 3.153292E+04, 3.265317E+04, 3.380534E+04, 3.499009E+04,
3.620811E+04, 3.746011E+04, 3.874677E+04, 4.006882E+04, 4.142697E+04, 4.282196E+04,
4.425451E+04, 4.572539E+04, 4.723535E+04, 4.878515E+04, 5.037557E+04, 5.200740E+04,
5.368142E+04, 5.539845E+04, 5.715929E+04, 5.896477E+04, 6.081571E+04, 6.271296E+04,
6.465735E+04, 6.664976E+04, 6.869105E+04, 7.078209E+04, 7.292377E+04, 7.511697E+04,
7.736262E+04, 7.966160E+04, 8.201486E+04, 8.442330E+04, 8.688789E+04, 8.940955E+04,
9.198924E+04, 9.462794E+04, 9.732660E+04, 1.000862E+05, 1.029078E+05, 1.057923E+05,
1.087407E+05, 1.117541E+05, 1.148335E+05, 1.179799E+05, 1.211944E+05, 1.244779E+05,
1.278316E+05, 1.312566E+05, 1.347538E+05, 1.383243E+05, 1.419693E+05, 1.456898E+05,
1.494870E+05, 1.533619E+05, 1.573156E+05, 1.613494E+05, 1.654642E+05, 1.696612E+05,
1.739416E+05, 1.783064E+05, 1.827570E+05, 1.872943E+05, 1.919196E+05, 1.966340E+05,
2.014388E+05, 2.063350E+05, 2.113239E+05, 2.164066E+05, 2.215843E+05, 2.268583E+05,
2.322298E+05, 2.376998E+05, 2.432697E+05, 2.489407E+05, 2.547139E+05, 2.605906E+05,
2.665720E+05, 2.726593E+05, 2.788538E+05, 2.851567E+05, 2.915692E+05, 2.980925E+05,
3.047279E+05, 3.114766E+05, 3.183399E+05, 3.253190E+05, 3.324151E+05, 3.396295E+05,
3.469634E+05, 3.544181E+05, 3.619948E+05, 3.696947E+05, 3.775191E+05, 3.854693E+05,
3.935464E+05, 4.017517E+05, 4.100865E+05, 4.185520E+05, 4.271494E+05, 4.358800E+05,
4.447450E+05, 4.537456E+05, 4.628831E+05, 4.721587E+05, 4.815736E+05, 4.911291E+05,
5.008263E+05, 5.106664E+05, 5.206508E+05, 5.307806E+05, 5.410569E+05, 5.514811E+05,
5.620543E+05, 5.727776E+05, 5.836524E+05, 5.946797E+05, 6.058608E+05, 6.171968E+05,
6.286889E+05, 6.403382E+05, 6.521460E+05, 6.641133E+05, 6.762413E+05, 6.885312E+05,
7.009841E+05, 7.136011E+05, 7.263834E+05, 7.393320E+05, 7.524481E+05, 7.657327E+05,
7.791870E+05, 7.928120E+05, 8.066089E+05, 8.205787E+05, 8.347224E+05, 8.490412E+05,
8.635361E+05, 8.782080E+05, 8.930581E+05, 9.080874E+05, 9.232969E+05, 9.386875E+05,
9.542604E+05, 9.700165E+05, 9.859567E+05, 1.002082E+06, 1.018394E+06,
])
# ---------------------- M = 3, I = 1 ---------------------------
M = 3
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
7.847400E-01, 5.870075E+01, 1.653093E+02, 3.033348E+02, 4.668337E+02, 6.523999E+02,
8.578395E+02, 1.081788E+03, 1.323572E+03, 1.583129E+03, 1.860885E+03, 2.157688E+03,
2.474797E+03, 2.813601E+03, 3.175846E+03, 3.563351E+03, 3.978042E+03, 4.422045E+03,
4.897528E+03, 5.406676E+03, 5.951807E+03, 6.535268E+03, 7.159589E+03, 7.827197E+03,
8.540615E+03, 9.302558E+03, 1.011573E+04, 1.098290E+04, 1.190686E+04, 1.289056E+04,
1.393708E+04, 1.504939E+04, 1.623081E+04, 1.748441E+04, 1.881364E+04, 2.022192E+04,
2.171273E+04, 2.328963E+04, 2.495636E+04, 2.671676E+04, 2.857462E+04, 3.053392E+04,
3.259874E+04, 3.477325E+04, 3.706184E+04, 3.946872E+04, 4.199829E+04, 4.465523E+04,
4.744417E+04, 5.037000E+04, 5.343750E+04, 5.665155E+04, 6.001736E+04, 6.354022E+04,
6.722531E+04, 7.107790E+04, 7.510385E+04, 7.930836E+04, 8.369781E+04, 8.827733E+04,
9.305350E+04, 9.803196E+04, 1.032190E+05, 1.086212E+05, 1.142448E+05, 1.200965E+05,
1.261830E+05, 1.325109E+05, 1.390870E+05, 1.459185E+05, 1.530127E+05, 1.603767E+05,
1.680182E+05, 1.759443E+05, 1.841627E+05, 1.926817E+05, 2.015087E+05, 2.106519E+05,
2.201195E+05, 2.299199E+05, 2.400610E+05, 2.505524E+05, 2.614014E+05, 2.726184E+05,
2.842108E+05, 2.961892E+05, 3.085615E+05, 3.213380E+05, 3.345278E+05, 3.481407E+05,
3.621862E+05, 3.766745E+05, 3.916156E+05, 4.070192E+05, 4.228968E+05, 4.392584E+05,
4.561139E+05, 4.734750E+05, 4.913523E+05, 5.097566E+05, 5.286996E+05, 5.481927E+05,
5.682468E+05, 5.888737E+05, 6.100856E+05, 6.318949E+05, 6.543128E+05, 6.773516E+05,
7.010245E+05, 7.253433E+05, 7.503210E+05, 7.759709E+05, 8.023056E+05, 8.293382E+05,
8.570820E+05, 8.855514E+05, 9.147587E+05, 9.447191E+05, 9.754458E+05, 1.006954E+06,
1.039256E+06, 1.072368E+06, 1.106304E+06, 1.141080E+06, 1.176708E+06, 1.213207E+06,
1.250589E+06, 1.288871E+06, 1.328070E+06, 1.368200E+06, 1.409276E+06, 1.451317E+06,
1.494338E+06, 1.538354E+06, 1.583385E+06, 1.629444E+06, 1.676552E+06, 1.724723E+06,
1.773976E+06, 1.824329E+06, 1.875798E+06, 1.928402E+06, 1.982159E+06, 2.037088E+06,
2.093206E+06, 2.150533E+06, 2.209086E+06, 2.268887E+06, 2.329953E+06, 2.392303E+06,
2.455959E+06, 2.520938E+06, 2.587261E+06, 2.654949E+06, 2.724021E+06, 2.794498E+06,
2.866401E+06, 2.939752E+06, 3.014570E+06, 3.090877E+06, 3.168695E+06, 3.248044E+06,
3.328948E+06, 3.411429E+06, 3.495507E+06, 3.581206E+06, 3.668550E+06, 3.757559E+06,
3.848259E+06, 3.940671E+06, 4.034818E+06, 4.130727E+06, 4.228419E+06, 4.327918E+06,
4.429248E+06, 4.532437E+06,
])
# ---------------------- M = 3, I = 2 ---------------------------
M = 3
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.659650E+00, 1.252867E+02, 3.529131E+02, 6.476329E+02, 9.967517E+02, 1.393009E+03,
1.831769E+03, 2.310206E+03, 2.827051E+03, 3.382309E+03, 3.977121E+03, 4.613505E+03,
5.294212E+03, 6.022501E+03, 6.802194E+03, 7.637375E+03, 8.532274E+03, 9.491628E+03,
1.052016E+04, 1.162272E+04, 1.280444E+04, 1.407051E+04, 1.542641E+04, 1.687762E+04,
1.842974E+04, 2.008860E+04, 2.186018E+04, 2.375072E+04, 2.576662E+04, 2.791400E+04,
3.019968E+04, 3.263083E+04, 3.521391E+04, 3.795663E+04, 4.086590E+04, 4.394976E+04,
4.721594E+04, 5.067202E+04, 5.432657E+04, 5.818808E+04, 6.226493E+04, 6.656593E+04,
7.110012E+04, 7.587705E+04, 8.090577E+04, 8.619627E+04, 9.175872E+04, 9.760273E+04,
1.037389E+05, 1.101777E+05, 1.169305E+05, 1.240078E+05, 1.314214E+05, 1.391823E+05,
1.473028E+05, 1.557947E+05, 1.646703E+05, 1.739424E+05, 1.836239E+05, 1.937268E+05,
2.042656E+05, 2.152535E+05, 2.267043E+05, 2.386323E+05, 2.510512E+05, 2.639756E+05,
2.774213E+05, 2.914026E+05, 3.059358E+05, 3.210359E+05, 3.367181E+05, 3.530002E+05,
3.698980E+05, 3.874283E+05, 4.056074E+05, 4.244543E+05, 4.439858E+05, 4.642198E+05,
4.851738E+05, 5.068680E+05, 5.293193E+05, 5.525478E+05, 5.765737E+05, 6.014149E+05,
6.270935E+05, 6.536272E+05, 6.810391E+05, 7.093488E+05, 7.385776E+05, 7.687471E+05,
7.998788E+05, 8.319959E+05, 8.651198E+05, 8.992744E+05, 9.344815E+05, 9.707647E+05,
1.008148E+06, 1.046657E+06, 1.086313E+06, 1.127143E+06, 1.169172E+06, 1.212425E+06,
1.256927E+06, 1.302705E+06, 1.349784E+06, 1.398192E+06, 1.447956E+06, 1.499104E+06,
1.551662E+06, 1.605658E+06, 1.661122E+06, 1.718084E+06, 1.776569E+06, 1.836610E+06,
1.898235E+06, 1.961475E+06, 2.026362E+06, 2.092923E+06, 2.161193E+06, 2.231202E+06,
2.302981E+06, 2.376565E+06, 2.451985E+06, 2.529274E+06, 2.608467E+06, 2.689597E+06,
2.772695E+06, 2.857802E+06, 2.944948E+06, 3.034171E+06, 3.125505E+06, 3.218987E+06,
3.314654E+06, 3.412542E+06, 3.512687E+06, 3.615129E+06, 3.719906E+06, 3.827055E+06,
3.936617E+06, 4.048628E+06, 4.163130E+06, 4.280164E+06, 4.399769E+06, 4.521984E+06,
4.646853E+06, 4.774418E+06, 4.904720E+06, 5.037801E+06, 5.173705E+06, 5.312474E+06,
5.454153E+06, 5.598785E+06, 5.746414E+06, 5.897090E+06, 6.050851E+06, 6.207749E+06,
6.367826E+06, 6.531132E+06, 6.697714E+06, 6.867614E+06, 7.040889E+06, 7.217578E+06,
7.397739E+06, 7.581418E+06, 7.768659E+06, 7.959519E+06, 8.154048E+06, 8.352294E+06,
8.554311E+06, 8.760149E+06, 8.969865E+06, 9.183502E+06, 9.401127E+06, 9.622785E+06,
9.848527E+06, 1.007842E+07,
])
# ---------------------- M = 3, I = 3 ---------------------------
M = 3
I = 3
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
8.185900E-01, 6.125610E+01, 1.725127E+02, 3.165570E+02, 4.871861E+02, 6.808519E+02,
8.952625E+02, 1.129039E+03, 1.381537E+03, 1.652726E+03, 1.943193E+03, 2.253943E+03,
2.586326E+03, 2.942026E+03, 3.322888E+03, 3.730960E+03, 4.168424E+03, 4.637562E+03,
5.140769E+03, 5.680414E+03, 6.259100E+03, 6.879368E+03, 7.543867E+03, 8.255453E+03,
9.016771E+03, 9.830870E+03, 1.070057E+04, 1.162904E+04, 1.261929E+04, 1.367462E+04,
1.479833E+04, 1.599377E+04, 1.726436E+04, 1.861379E+04, 2.004575E+04, 2.156398E+04,
2.317217E+04, 2.487462E+04, 2.667503E+04, 2.857789E+04, 3.058721E+04, 3.270766E+04,
3.494358E+04, 3.729938E+04, 3.978003E+04, 4.239034E+04, 4.513515E+04, 4.801943E+04,
5.104863E+04, 5.422773E+04, 5.756221E+04, 6.105763E+04, 6.471951E+04, 6.855364E+04,
7.256596E+04, 7.676252E+04, 8.114927E+04, 8.573247E+04, 9.051853E+04, 9.551399E+04,
1.007252E+05, 1.061593E+05, 1.118231E+05, 1.177230E+05, 1.238669E+05, 1.302620E+05,
1.369151E+05, 1.438344E+05, 1.510269E+05, 1.585011E+05, 1.662646E+05, 1.743254E+05,
1.826918E+05, 1.913721E+05, 2.003749E+05, 2.097086E+05, 2.193821E+05, 2.294048E+05,
2.397852E+05, 2.505325E+05, 2.616565E+05, 2.731663E+05, 2.850718E+05, 2.973825E+05,
3.101087E+05, 3.232600E+05, 3.368470E+05, 3.508807E+05, 3.653706E+05, 3.803279E+05,
3.957632E+05, 4.116883E+05, 4.281136E+05, 4.450504E+05, 4.625108E+05, 4.805059E+05,
4.990478E+05, 5.181485E+05, 5.378202E+05, 5.580750E+05, 5.789261E+05, 6.003846E+05,
6.224646E+05, 6.451784E+05, 6.685396E+05, 6.925614E+05, 7.172569E+05, 7.426405E+05,
7.687248E+05, 7.955251E+05, 8.230548E+05, 8.513281E+05, 8.803602E+05, 9.101655E+05,
9.407590E+05, 9.721548E+05, 1.004370E+06, 1.037418E+06, 1.071315E+06, 1.106078E+06,
1.141721E+06, 1.178261E+06, 1.215715E+06, 1.254098E+06, 1.293428E+06, 1.333722E+06,
1.374996E+06, 1.417268E+06, 1.460554E+06, 1.504874E+06, 1.550244E+06, 1.596682E+06,
1.644207E+06, 1.692838E+06, 1.742592E+06, 1.793488E+06, 1.845546E+06, 1.898785E+06,
1.953223E+06, 2.008881E+06, 2.065779E+06, 2.123935E+06, 2.183371E+06, 2.244106E+06,
2.306163E+06, 2.369560E+06, 2.434319E+06, 2.500462E+06, 2.568009E+06, 2.636982E+06,
2.707404E+06, 2.779295E+06, 2.852679E+06, 2.927578E+06, 3.004013E+06, 3.082010E+06,
3.161589E+06, 3.242776E+06, 3.325591E+06, 3.410061E+06, 3.496209E+06, 3.584059E+06,
3.673634E+06, 3.764960E+06, 3.858061E+06, 3.952965E+06, 4.049693E+06, 4.148272E+06,
4.248730E+06, 4.351088E+06, 4.455377E+06, 4.561620E+06, 4.669848E+06, 4.780082E+06,
4.892352E+06, 5.006686E+06,
])
# ---------------------- M = 3, I = 4 ---------------------------
M = 3
I = 4
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
9.693860E+00, 7.286110E+02, 2.052138E+03, 3.765742E+03, 5.795622E+03, 8.099566E+03,
1.065031E+04, 1.343144E+04, 1.643497E+04, 1.966034E+04, 2.311386E+04, 2.680652E+04,
3.075398E+04, 3.497494E+04, 3.949041E+04, 4.432421E+04, 4.950089E+04, 5.504705E+04,
6.098959E+04, 6.735674E+04, 7.417719E+04, 8.148200E+04, 8.930109E+04, 9.766575E+04,
1.066095E+05, 1.161652E+05, 1.263658E+05, 1.372482E+05, 1.488478E+05, 1.612019E+05,
1.743472E+05, 1.883247E+05, 2.031740E+05, 2.189350E+05, 2.356501E+05, 2.533639E+05,
2.721204E+05, 2.919662E+05, 3.129454E+05, 3.351083E+05, 3.585027E+05, 3.831788E+05,
4.091905E+05, 4.365882E+05, 4.654258E+05, 4.957624E+05, 5.276496E+05, 5.611468E+05,
5.963170E+05, 6.332148E+05, 6.719059E+05, 7.124520E+05, 7.549189E+05, 7.993710E+05,
8.458764E+05, 8.945049E+05, 9.453249E+05, 9.984083E+05, 1.053826E+06, 1.111656E+06,
1.171972E+06, 1.234856E+06, 1.300379E+06, 1.368623E+06, 1.439674E+06, 1.513611E+06,
1.590524E+06, 1.670492E+06, 1.753610E+06, 1.839962E+06, 1.929642E+06, 2.022739E+06,
2.119351E+06, 2.219570E+06, 2.323499E+06, 2.431227E+06, 2.542864E+06, 2.658513E+06,
2.778268E+06, 2.902239E+06, 3.030540E+06, 3.163267E+06, 3.300539E+06, 3.442465E+06,
3.589168E+06, 3.740750E+06, 3.897328E+06, 4.059035E+06, 4.225983E+06, 4.398292E+06,
4.576090E+06, 4.759502E+06, 4.948660E+06, 5.143690E+06, 5.344725E+06, 5.551892E+06,
5.765331E+06, 5.985180E+06, 6.211579E+06, 6.444666E+06, 6.684586E+06, 6.931482E+06,
7.185500E+06, 7.446782E+06, 7.715490E+06, 7.991764E+06, 8.275772E+06, 8.567664E+06,
8.867593E+06, 9.175712E+06, 9.492203E+06, 9.817227E+06, 1.015093E+07, 1.049349E+07,
1.084509E+07, 1.120589E+07, 1.157607E+07, 1.195579E+07, 1.234524E+07, 1.274460E+07,
1.315405E+07, 1.357378E+07, 1.400397E+07, 1.444480E+07, 1.489648E+07, 1.535919E+07,
1.583314E+07, 1.631850E+07, 1.681549E+07, 1.732430E+07, 1.784514E+07, 1.837821E+07,
1.892372E+07, 1.948191E+07, 2.005293E+07, 2.063704E+07, 2.123444E+07, 2.184536E+07,
2.247001E+07, 2.310862E+07, 2.376142E+07, 2.442864E+07, 2.511048E+07, 2.580720E+07,
2.651904E+07, 2.724621E+07, 2.798898E+07, 2.874757E+07, 2.952224E+07, 3.031321E+07,
3.112077E+07, 3.194514E+07, 3.278657E+07, 3.364534E+07, 3.452170E+07, 3.541590E+07,
3.632821E+07, 3.725890E+07, 3.820824E+07, 3.917648E+07, 4.016391E+07, 4.117082E+07,
4.219747E+07, 4.324413E+07, 4.431109E+07, 4.539866E+07, 4.650710E+07, 4.763672E+07,
4.878780E+07, 4.996063E+07, 5.115550E+07, 5.237277E+07, 5.361268E+07, 5.487554E+07,
5.616170E+07, 5.747145E+07,
])
# ---------------------- M = 3, I = 5 ---------------------------
M = 3
I = 5
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
4.813030E+00, 3.600892E+02, 1.014082E+03, 1.860805E+03, 2.863799E+03, 4.002166E+03,
5.262502E+03, 6.636539E+03, 8.120243E+03, 9.713429E+03, 1.141910E+04, 1.324287E+04,
1.519237E+04, 1.727719E+04, 1.950780E+04, 2.189589E+04, 2.445388E+04, 2.719480E+04,
3.013257E+04, 3.328074E+04, 3.665424E+04, 4.026795E+04, 4.413675E+04, 4.827698E+04,
5.270423E+04, 5.743513E+04, 6.248716E+04, 6.787719E+04, 7.362388E+04, 7.974527E+04,
8.626022E+04, 9.318860E+04, 1.005494E+05, 1.083638E+05, 1.166537E+05, 1.254391E+05,
1.347427E+05, 1.445876E+05, 1.549967E+05, 1.659941E+05, 1.776035E+05, 1.898519E+05,
2.027633E+05, 2.163637E+05, 2.306819E+05, 2.457443E+05, 2.615788E+05, 2.782148E+05,
2.956814E+05, 3.140094E+05, 3.332292E+05, 3.533726E+05, 3.744711E+05, 3.965582E+05,
4.196678E+05, 4.438332E+05, 4.690883E+05, 4.954722E+05, 5.230185E+05, 5.517647E+05,
5.817489E+05, 6.130100E+05, 6.455856E+05, 6.795180E+05, 7.148483E+05, 7.516143E+05,
7.898604E+05, 8.296320E+05, 8.709688E+05, 9.139178E+05, 9.585242E+05, 1.004832E+06,
1.052892E+06, 1.102747E+06, 1.154451E+06, 1.208049E+06, 1.263593E+06, 1.321131E+06,
1.380722E+06, 1.442410E+06, 1.506255E+06, 1.572304E+06, 1.640622E+06, 1.711256E+06,
1.784265E+06, 1.859711E+06, 1.937648E+06, 2.018139E+06, 2.101238E+06, 2.187013E+06,
2.275522E+06, 2.366832E+06, 2.461001E+06, 2.558097E+06, 2.658186E+06, 2.761333E+06,
2.867604E+06, 2.977071E+06, 3.089804E+06, 3.205868E+06, 3.325339E+06, 3.448284E+06,
3.574781E+06, 3.704902E+06, 3.838724E+06, 3.976317E+06, 4.117767E+06, 4.263141E+06,
4.412525E+06, 4.565996E+06, 4.723636E+06, 4.885531E+06, 5.051753E+06, 5.222396E+06,
5.397538E+06, 5.577272E+06, 5.761674E+06, 5.950846E+06, 6.144860E+06, 6.343822E+06,
6.547813E+06, 6.756924E+06, 6.971254E+06, 7.190894E+06, 7.415940E+06, 7.646489E+06,
7.882637E+06, 8.124482E+06, 8.372123E+06, 8.625660E+06, 8.885193E+06, 9.150831E+06,
9.422673E+06, 9.700822E+06, 9.985389E+06, 1.027648E+07, 1.057420E+07, 1.087865E+07,
1.118997E+07, 1.150823E+07, 1.183358E+07, 1.216611E+07, 1.250594E+07, 1.285320E+07,
1.320799E+07, 1.357043E+07, 1.394065E+07, 1.431876E+07, 1.470489E+07, 1.509916E+07,
1.550169E+07, 1.591261E+07, 1.633204E+07, 1.676013E+07, 1.719697E+07, 1.764274E+07,
1.809752E+07, 1.856147E+07, 1.903474E+07, 1.951743E+07, 2.000969E+07, 2.051167E+07,
2.102349E+07, 2.154530E+07, 2.207724E+07, 2.261945E+07, 2.317208E+07, 2.373528E+07,
2.430918E+07, 2.489392E+07, 2.548968E+07, 2.609659E+07, 2.671481E+07, 2.734449E+07,
2.798577E+07, 2.863883E+07,
])
# ---------------------- M = 3, I = 6 ---------------------------
M = 3
I = 6
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.732560E+00, 1.308439E+02, 3.685813E+02, 6.763945E+02, 1.041035E+03, 1.454902E+03,
1.913200E+03, 2.413032E+03, 2.953209E+03, 3.533915E+03, 4.156499E+03, 4.823380E+03,
5.537674E+03, 6.303045E+03, 7.123765E+03, 8.004388E+03, 8.949564E+03, 9.964533E+03,
1.105444E+04, 1.222466E+04, 1.348084E+04, 1.482862E+04, 1.627401E+04, 1.782304E+04,
1.948187E+04, 2.125695E+04, 2.315481E+04, 2.518216E+04, 2.734599E+04, 2.965343E+04,
3.211177E+04, 3.472865E+04, 3.751164E+04, 4.046894E+04, 4.360818E+04, 4.693842E+04,
5.046789E+04, 5.420551E+04, 5.816009E+04, 6.234127E+04, 6.675826E+04, 7.142107E+04,
7.633970E+04, 8.152423E+04, 8.698542E+04, 9.273349E+04, 9.877997E+04, 1.051355E+05,
1.118121E+05, 1.188218E+05, 1.261758E+05, 1.338869E+05, 1.419674E+05, 1.504307E+05,
1.592888E+05, 1.685564E+05, 1.782460E+05, 1.883721E+05, 1.989491E+05, 2.099906E+05,
2.215123E+05, 2.335285E+05, 2.460551E+05, 2.591078E+05, 2.727019E+05, 2.868541E+05,
3.015810E+05, 3.168993E+05, 3.328262E+05, 3.493782E+05, 3.665743E+05, 3.844320E+05,
4.029702E+05, 4.222066E+05, 4.421602E+05, 4.628517E+05, 4.842990E+05, 5.065236E+05,
5.295438E+05, 5.533829E+05, 5.780588E+05, 6.035954E+05, 6.300121E+05, 6.573328E+05,
6.855785E+05, 7.147714E+05, 7.449354E+05, 7.760936E+05, 8.082695E+05, 8.414866E+05,
8.757691E+05, 9.111436E+05, 9.476326E+05, 9.852625E+05, 1.024060E+06, 1.064049E+06,
1.105257E+06, 1.147712E+06, 1.191440E+06, 1.236468E+06, 1.282825E+06, 1.330539E+06,
1.379637E+06, 1.430151E+06, 1.482109E+06, 1.535539E+06, 1.590474E+06, 1.646944E+06,
1.704977E+06, 1.764609E+06, 1.825868E+06, 1.888788E+06, 1.953399E+06, 2.019738E+06,
2.087834E+06, 2.157723E+06, 2.229439E+06, 2.303016E+06, 2.378487E+06, 2.455892E+06,
2.535263E+06, 2.616637E+06, 2.700050E+06, 2.785540E+06, 2.873144E+06, 2.962900E+06,
3.054845E+06, 3.149018E+06, 3.245459E+06, 3.344205E+06, 3.445299E+06, 3.548780E+06,
3.654689E+06, 3.763068E+06, 3.873955E+06, 3.987395E+06, 4.103430E+06, 4.222104E+06,
4.343458E+06, 4.467538E+06, 4.594386E+06, 4.724049E+06, 4.856572E+06, 4.991999E+06,
5.130375E+06, 5.271750E+06, 5.416171E+06, 5.563681E+06, 5.714331E+06, 5.868170E+06,
6.025247E+06, 6.185609E+06, 6.349307E+06, 6.516392E+06, 6.686913E+06, 6.860922E+06,
7.038471E+06, 7.219613E+06, 7.404399E+06, 7.592883E+06, 7.785118E+06, 7.981157E+06,
8.181058E+06, 8.384875E+06, 8.592660E+06, 8.804475E+06, 9.020369E+06, 9.240407E+06,
9.464641E+06, 9.693130E+06, 9.925935E+06, 1.016311E+07, 1.040473E+07, 1.065083E+07,
1.090149E+07, 1.115676E+07,
])
# ---------------------- M = 3, I = 7 ---------------------------
M = 3
I = 7
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
8.789300E-01, 6.694173E+01, 1.886102E+02, 3.461476E+02, 5.327717E+02, 7.445995E+02,
9.791830E+02, 1.235085E+03, 1.511700E+03, 1.809143E+03, 2.128107E+03, 2.469769E+03,
2.835738E+03, 3.227819E+03, 3.648133E+03, 4.098969E+03, 4.582699E+03, 5.101875E+03,
5.659118E+03, 6.257169E+03, 6.898826E+03, 7.586970E+03, 8.324625E+03, 9.114762E+03,
9.960542E+03, 1.086520E+04, 1.183213E+04, 1.286461E+04, 1.396617E+04, 1.514050E+04,
1.639117E+04, 1.772203E+04, 1.913689E+04, 2.064001E+04, 2.223524E+04, 2.392689E+04,
2.571922E+04, 2.761689E+04, 2.962419E+04, 3.174614E+04, 3.398705E+04, 3.635233E+04,
3.884663E+04, 4.147543E+04, 4.424376E+04, 4.715716E+04, 5.022104E+04, 5.344119E+04,
5.682326E+04, 6.037348E+04, 6.409755E+04, 6.800167E+04, 7.209225E+04, 7.637581E+04,
8.085871E+04, 8.554797E+04, 9.045026E+04, 9.557287E+04, 1.009224E+05, 1.065065E+05,
1.123325E+05, 1.184081E+05, 1.247407E+05, 1.313384E+05, 1.382093E+05, 1.453615E+05,
1.528030E+05, 1.605426E+05, 1.685888E+05, 1.769504E+05, 1.856360E+05, 1.946552E+05,
2.040167E+05, 2.137304E+05, 2.238055E+05, 2.342516E+05, 2.450786E+05, 2.562968E+05,
2.679164E+05, 2.799473E+05, 2.924003E+05, 3.052861E+05, 3.186154E+05, 3.323992E+05,
3.466491E+05, 3.613758E+05, 3.765914E+05, 3.923068E+05, 4.085344E+05, 4.252868E+05,
4.425748E+05, 4.604120E+05, 4.788108E+05, 4.977832E+05, 5.173427E+05, 5.375024E+05,
5.582752E+05, 5.796753E+05, 6.017155E+05, 6.244101E+05, 6.477726E+05, 6.718181E+05,
6.965603E+05, 7.220136E+05, 7.481935E+05, 7.751140E+05, 8.027916E+05, 8.312397E+05,
8.604757E+05, 8.905141E+05, 9.213715E+05, 9.530639E+05, 9.856067E+05, 1.019018E+06,
1.053313E+06, 1.088509E+06, 1.124624E+06, 1.161674E+06, 1.199678E+06, 1.238652E+06,
1.278615E+06, 1.319584E+06, 1.361580E+06, 1.404619E+06, 1.448720E+06, 1.493902E+06,
1.540186E+06, 1.587589E+06, 1.636131E+06, 1.685832E+06, 1.736714E+06, 1.788795E+06,
1.842095E+06, 1.896636E+06, 1.952439E+06, 2.009525E+06, 2.067914E+06, 2.127629E+06,
2.188690E+06, 2.251121E+06, 2.314944E+06, 2.380180E+06, 2.446852E+06, 2.514985E+06,
2.584598E+06, 2.655719E+06, 2.728370E+06, 2.802572E+06, 2.878352E+06, 2.955734E+06,
3.034741E+06, 3.115400E+06, 3.197733E+06, 3.281769E+06, 3.367530E+06, 3.455043E+06,
3.544334E+06, 3.635429E+06, 3.728356E+06, 3.823139E+06, 3.919806E+06, 4.018383E+06,
4.118900E+06, 4.221384E+06, 4.325860E+06, 4.432360E+06, 4.540909E+06, 4.651539E+06,
4.764276E+06, 4.879150E+06, 4.996191E+06, 5.115428E+06, 5.236890E+06, 5.360610E+06,
5.486615E+06, 5.614939E+06,
])
# ---------------------- M = 3, I = 8 ---------------------------
M = 3
I = 8
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.018317E+01, 7.688685E+02, 2.165826E+03, 3.974547E+03, 6.117122E+03, 8.549075E+03,
1.124182E+04, 1.417849E+04, 1.735159E+04, 2.076146E+04, 2.441595E+04, 2.832833E+04,
3.251593E+04, 3.699983E+04, 4.180437E+04, 4.695509E+04, 5.247930E+04, 5.840655E+04,
6.476616E+04, 7.158997E+04, 7.890924E+04, 8.675751E+04, 9.516855E+04, 1.041762E+05,
1.138172E+05, 1.241287E+05, 1.351464E+05, 1.469105E+05, 1.594614E+05, 1.728378E+05,
1.870849E+05, 2.022417E+05, 2.183567E+05, 2.354731E+05, 2.536375E+05, 2.728988E+05,
2.933058E+05, 3.149078E+05, 3.377591E+05, 3.619112E+05, 3.874208E+05, 4.143397E+05,
4.427289E+05, 4.726444E+05, 5.041454E+05, 5.372972E+05, 5.721616E+05, 6.087995E+05,
6.472799E+05, 6.876690E+05, 7.300362E+05, 7.744501E+05, 8.209831E+05, 8.697093E+05,
9.207011E+05, 9.740386E+05, 1.029797E+06, 1.088057E+06, 1.148900E+06, 1.212405E+06,
1.278663E+06, 1.347752E+06, 1.419769E+06, 1.494792E+06, 1.572920E+06, 1.654247E+06,
1.738862E+06, 1.826861E+06, 1.918342E+06, 2.013409E+06, 2.112162E+06, 2.214696E+06,
2.321129E+06, 2.431558E+06, 2.546090E+06, 2.664846E+06, 2.787926E+06, 2.915445E+06,
3.047527E+06, 3.184286E+06, 3.325838E+06, 3.472305E+06, 3.623810E+06, 3.780482E+06,
3.942443E+06, 4.109824E+06, 4.282759E+06, 4.461371E+06, 4.645800E+06, 4.836192E+06,
5.032673E+06, 5.235386E+06, 5.444471E+06, 5.660087E+06, 5.882366E+06, 6.111462E+06,
6.347516E+06, 6.590696E+06, 6.841155E+06, 7.099044E+06, 7.364517E+06, 7.637744E+06,
7.918894E+06, 8.208116E+06, 8.505584E+06, 8.811474E+06, 9.125951E+06, 9.449192E+06,
9.781375E+06, 1.012267E+07, 1.047327E+07, 1.083334E+07, 1.120309E+07, 1.158269E+07,
1.197233E+07, 1.237221E+07, 1.278251E+07, 1.320344E+07, 1.363520E+07, 1.407798E+07,
1.453199E+07, 1.499743E+07, 1.547452E+07, 1.596346E+07, 1.646446E+07, 1.697775E+07,
1.750353E+07, 1.804203E+07, 1.859347E+07, 1.915807E+07, 1.973607E+07, 2.032768E+07,
2.093316E+07, 2.155272E+07, 2.218661E+07, 2.283506E+07, 2.349832E+07, 2.417662E+07,
2.487023E+07, 2.557938E+07, 2.630433E+07, 2.704534E+07, 2.780265E+07, 2.857654E+07,
2.936727E+07, 3.017509E+07, 3.100027E+07, 3.184310E+07, 3.270384E+07, 3.358276E+07,
3.448014E+07, 3.539626E+07, 3.633142E+07, 3.728588E+07, 3.825994E+07, 3.925390E+07,
4.026805E+07, 4.130268E+07, 4.235810E+07, 4.343460E+07, 4.453249E+07, 4.565210E+07,
4.679370E+07, 4.795764E+07, 4.914420E+07, 5.035375E+07, 5.158655E+07, 5.284297E+07,
5.412334E+07, 5.542796E+07, 5.675719E+07, 5.811133E+07, 5.949078E+07, 6.089583E+07,
6.232682E+07, 6.378415E+07,
])
# ---------------------- M = 3, I = 9 ---------------------------
M = 3
I = 9
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.025939E+01, 7.780853E+02, 2.192024E+03, 4.022766E+03, 6.191507E+03, 8.653040E+03,
1.137884E+04, 1.435181E+04, 1.756428E+04, 2.101726E+04, 2.471805E+04, 2.867999E+04,
3.292087E+04, 3.746130E+04, 4.232569E+04, 4.753963E+04, 5.313097E+04, 5.912840E+04,
6.556172E+04, 7.246219E+04, 7.986305E+04, 8.779593E+04, 9.629532E+04, 1.053959E+05,
1.151338E+05, 1.255461E+05, 1.366706E+05, 1.485447E+05, 1.612110E+05, 1.747082E+05,
1.890807E+05, 2.043701E+05, 2.206199E+05, 2.378786E+05, 2.561919E+05, 2.756073E+05,
2.961737E+05, 3.179441E+05, 3.409693E+05, 3.653014E+05, 3.909969E+05, 4.181098E+05,
4.467016E+05, 4.768262E+05, 5.085454E+05, 5.419201E+05, 5.770169E+05, 6.138970E+05,
6.526250E+05, 6.932718E+05, 7.359051E+05, 7.805932E+05, 8.274118E+05, 8.764327E+05,
9.277281E+05, 9.813788E+05, 1.037460E+06, 1.096053E+06, 1.157238E+06, 1.221100E+06,
1.287723E+06, 1.357190E+06, 1.429589E+06, 1.505014E+06, 1.583553E+06, 1.665298E+06,
1.750344E+06, 1.838791E+06, 1.930734E+06, 2.026272E+06, 2.125506E+06, 2.228539E+06,
2.335477E+06, 2.446431E+06, 2.561504E+06, 2.680806E+06, 2.804454E+06, 2.932553E+06,
3.065232E+06, 3.202599E+06, 3.344773E+06, 3.491880E+06, 3.644044E+06, 3.801383E+06,
3.964036E+06, 4.132119E+06, 4.305769E+06, 4.485124E+06, 4.670306E+06, 4.861464E+06,
5.058736E+06, 5.262256E+06, 5.472173E+06, 5.688624E+06, 5.911762E+06, 6.141735E+06,
6.378700E+06, 6.622796E+06, 6.874190E+06, 7.133033E+06, 7.399489E+06, 7.673712E+06,
7.955873E+06, 8.246134E+06, 8.544672E+06, 8.851639E+06, 9.167217E+06, 9.491582E+06,
9.824914E+06, 1.016738E+07, 1.051917E+07, 1.088046E+07, 1.125145E+07, 1.163231E+07,
1.202323E+07, 1.242442E+07, 1.283606E+07, 1.325835E+07, 1.369150E+07, 1.413568E+07,
1.459113E+07, 1.505804E+07, 1.553661E+07, 1.602706E+07, 1.652961E+07, 1.704446E+07,
1.757184E+07, 1.811196E+07, 1.866504E+07, 1.923134E+07, 1.981104E+07, 2.040440E+07,
2.101163E+07, 2.163299E+07, 2.226870E+07, 2.291900E+07, 2.358413E+07, 2.426437E+07,
2.495991E+07, 2.567104E+07, 2.639800E+07, 2.714105E+07, 2.790045E+07, 2.867645E+07,
2.946930E+07, 3.027929E+07, 3.110670E+07, 3.195175E+07, 3.281477E+07, 3.369600E+07,
3.459571E+07, 3.551422E+07, 3.645178E+07, 3.740870E+07, 3.838525E+07, 3.938174E+07,
4.039843E+07, 4.143568E+07, 4.249373E+07, 4.357290E+07, 4.467351E+07, 4.579585E+07,
4.694025E+07, 4.810702E+07, 4.929646E+07, 5.050889E+07, 5.174464E+07, 5.300407E+07,
5.428747E+07, 5.559516E+07, 5.692750E+07, 5.828480E+07, 5.966743E+07, 6.107572E+07,
6.251002E+07, 6.397068E+07,
])
# ---------------------- M = 3, I = 10 ---------------------------
M = 3
I = 10
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.011611E+01, 7.606432E+02, 2.142444E+03, 3.931508E+03, 6.050781E+03, 8.456192E+03,
1.111961E+04, 1.402401E+04, 1.716179E+04, 2.053370E+04, 2.414710E+04, 2.801498E+04,
3.215563E+04, 3.658950E+04, 4.134081E+04, 4.643537E+04, 5.190096E+04, 5.776617E+04,
6.406074E+04, 7.081585E+04, 7.806331E+04, 8.583661E+04, 9.416875E+04, 1.030942E+05,
1.126489E+05, 1.228698E+05, 1.337943E+05, 1.454598E+05, 1.579072E+05, 1.711776E+05,
1.853113E+05, 2.003519E+05, 2.163455E+05, 2.333335E+05, 2.513666E+05, 2.704892E+05,
2.907529E+05, 3.122071E+05, 3.349040E+05, 3.588951E+05, 3.842348E+05, 4.109803E+05,
4.391888E+05, 4.689179E+05, 5.002271E+05, 5.331765E+05, 5.678301E+05, 6.042531E+05,
6.425100E+05, 6.826686E+05, 7.247962E+05, 7.689631E+05, 8.152419E+05, 8.637038E+05,
9.144236E+05, 9.674780E+05, 1.022945E+06, 1.080905E+06, 1.141440E+06, 1.204627E+06,
1.270556E+06, 1.339309E+06, 1.410973E+06, 1.485641E+06, 1.563401E+06, 1.644344E+06,
1.728569E+06, 1.816162E+06, 1.907235E+06, 2.001877E+06, 2.100190E+06, 2.202280E+06,
2.308247E+06, 2.418202E+06, 2.532251E+06, 2.650506E+06, 2.773076E+06, 2.900076E+06,
3.031614E+06, 3.167824E+06, 3.308806E+06, 3.454693E+06, 3.605607E+06, 3.761672E+06,
3.923009E+06, 4.089756E+06, 4.262030E+06, 4.439977E+06, 4.623729E+06, 4.813416E+06,
5.009182E+06, 5.211159E+06, 5.419501E+06, 5.634346E+06, 5.855846E+06, 6.084142E+06,
6.319381E+06, 6.561730E+06, 6.811327E+06, 7.068344E+06, 7.332930E+06, 7.605253E+06,
7.885465E+06, 8.173747E+06, 8.470249E+06, 8.775154E+06, 9.088627E+06, 9.410835E+06,
9.741974E+06, 1.008220E+07, 1.043171E+07, 1.079068E+07, 1.115929E+07, 1.153774E+07,
1.192620E+07, 1.232488E+07, 1.273397E+07, 1.315366E+07, 1.358416E+07, 1.402564E+07,
1.447834E+07, 1.494244E+07, 1.541817E+07, 1.590572E+07, 1.640531E+07, 1.691716E+07,
1.744149E+07, 1.797850E+07, 1.852842E+07, 1.909149E+07, 1.966793E+07, 2.025794E+07,
2.086181E+07, 2.147971E+07, 2.211193E+07, 2.275867E+07, 2.342019E+07, 2.409675E+07,
2.478856E+07, 2.549589E+07, 2.621900E+07, 2.695813E+07, 2.771354E+07, 2.848549E+07,
2.927424E+07, 3.008006E+07, 3.090322E+07, 3.174399E+07, 3.260262E+07, 3.347942E+07,
3.437463E+07, 3.528858E+07, 3.622149E+07, 3.717369E+07, 3.814547E+07, 3.913711E+07,
4.014888E+07, 4.118112E+07, 4.223410E+07, 4.330813E+07, 4.440353E+07, 4.552057E+07,
4.665960E+07, 4.782093E+07, 4.900486E+07, 5.021170E+07, 5.144178E+07, 5.269544E+07,
5.397301E+07, 5.527477E+07, 5.660111E+07, 5.795235E+07, 5.932882E+07, 6.073086E+07,
6.215883E+07, 6.361306E+07,
])
# ---------------------- M = 3, I = 11 ---------------------------
M = 3
I = 11
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
5.946791E+01, 4.470503E+03, 1.259147E+04, 2.310592E+04, 3.556102E+04, 4.969775E+04,
6.535012E+04, 8.241681E+04, 1.008519E+05, 1.206550E+05, 1.418675E+05, 1.645612E+05,
1.888379E+05, 2.148151E+05, 2.426307E+05, 2.724293E+05, 3.043712E+05, 3.386193E+05,
3.753466E+05, 4.147317E+05, 4.569565E+05, 5.022089E+05, 5.506812E+05, 6.025756E+05,
6.580938E+05, 7.174414E+05, 7.808443E+05, 8.485166E+05, 9.206880E+05, 9.975858E+05,
1.079455E+06, 1.166548E+06, 1.259104E+06, 1.357392E+06, 1.461671E+06, 1.572220E+06,
1.689327E+06, 1.813266E+06, 1.944345E+06, 2.082848E+06, 2.229110E+06, 2.383425E+06,
2.546143E+06, 2.717572E+06, 2.898074E+06, 3.087996E+06, 3.287693E+06, 3.497522E+06,
3.717868E+06, 3.949102E+06, 4.191641E+06, 4.445858E+06, 4.712167E+06, 4.991009E+06,
5.282761E+06, 5.587915E+06, 5.906867E+06, 6.240090E+06, 6.588050E+06, 6.951201E+06,
7.330046E+06, 7.725058E+06, 8.136736E+06, 8.565590E+06, 9.012166E+06, 9.476920E+06,
9.960460E+06, 1.046329E+07, 1.098601E+07, 1.152911E+07, 1.209326E+07, 1.267897E+07,
1.328686E+07, 1.391754E+07, 1.457164E+07, 1.524976E+07, 1.595255E+07, 1.668068E+07,
1.743475E+07, 1.821548E+07, 1.902353E+07, 1.985957E+07, 2.072435E+07, 2.161852E+07,
2.254284E+07, 2.349807E+07, 2.448487E+07, 2.550405E+07, 2.655638E+07, 2.764263E+07,
2.876356E+07, 2.992002E+07, 3.111277E+07, 3.234267E+07, 3.361055E+07, 3.491722E+07,
3.626357E+07, 3.765045E+07, 3.907876E+07, 4.054937E+07, 4.206321E+07, 4.362119E+07,
4.522422E+07, 4.687322E+07, 4.856921E+07, 5.031312E+07, 5.210590E+07, 5.394861E+07,
5.584216E+07, 5.778765E+07, 5.978600E+07, 6.183837E+07, 6.394578E+07, 6.610924E+07,
6.832982E+07, 7.060868E+07, 7.294696E+07, 7.534565E+07, 7.780593E+07, 8.032895E+07,
8.291590E+07, 8.556789E+07, 8.828611E+07, 9.107179E+07, 9.392614E+07, 9.685037E+07,
9.984571E+07, 1.029134E+08, 1.060547E+08, 1.092709E+08, 1.125633E+08, 1.159332E+08,
1.193819E+08, 1.229108E+08, 1.265212E+08, 1.302143E+08, 1.339918E+08, 1.378548E+08,
1.418050E+08, 1.458435E+08, 1.499719E+08, 1.541916E+08, 1.585041E+08, 1.629109E+08,
1.674134E+08, 1.720131E+08, 1.767117E+08, 1.815105E+08, 1.864112E+08, 1.914154E+08,
1.965245E+08, 2.017402E+08, 2.070642E+08, 2.124980E+08, 2.180433E+08, 2.237017E+08,
2.294748E+08, 2.353645E+08, 2.413725E+08, 2.475002E+08, 2.537497E+08, 2.601226E+08,
2.666206E+08, 2.732456E+08, 2.799992E+08, 2.868835E+08, 2.939002E+08, 3.010511E+08,
3.083381E+08, 3.157630E+08, 3.233278E+08, 3.310344E+08, 3.388847E+08, 3.468806E+08,
3.550242E+08, 3.633173E+08,
])
# ---------------------- M = 3, I = 12 ---------------------------
M = 3
I = 12
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
2.995096E+01, 2.261799E+03, 6.371201E+03, 1.169187E+04, 1.799462E+04, 2.514837E+04,
3.306945E+04, 4.170726E+04, 5.103813E+04, 6.106308E+04, 7.180236E+04, 8.329235E+04,
9.558279E+04, 1.087346E+05, 1.228140E+05, 1.378944E+05, 1.540562E+05, 1.713824E+05,
1.899567E+05, 2.098700E+05, 2.312133E+05, 2.540827E+05, 2.785728E+05, 3.047847E+05,
3.328220E+05, 3.627880E+05, 3.947933E+05, 4.289450E+05, 4.653615E+05, 5.041570E+05,
5.454511E+05, 5.893697E+05, 6.360411E+05, 6.855926E+05, 7.381579E+05, 7.938731E+05,
8.528868E+05, 9.153318E+05, 9.813678E+05, 1.051138E+06, 1.124804E+06, 1.202521E+06,
1.284451E+06, 1.370767E+06, 1.461638E+06, 1.557240E+06, 1.657755E+06, 1.763358E+06,
1.874240E+06, 1.990604E+06, 2.112627E+06, 2.240529E+06, 2.374501E+06, 2.514755E+06,
2.661508E+06, 2.814979E+06, 2.975376E+06, 3.142946E+06, 3.317913E+06, 3.500504E+06,
3.690974E+06, 3.889547E+06, 4.096506E+06, 4.312075E+06, 4.536516E+06, 4.770115E+06,
5.013131E+06, 5.265828E+06, 5.528484E+06, 5.801396E+06, 6.084839E+06, 6.379123E+06,
6.684533E+06, 7.001385E+06, 7.329976E+06, 7.670618E+06, 8.023632E+06, 8.389355E+06,
8.768103E+06, 9.160211E+06, 9.566016E+06, 9.985881E+06, 1.042016E+07, 1.086916E+07,
1.133330E+07, 1.181291E+07, 1.230839E+07, 1.282011E+07, 1.334843E+07, 1.389376E+07,
1.445651E+07, 1.503703E+07, 1.563578E+07, 1.625316E+07, 1.688956E+07, 1.754542E+07,
1.822117E+07, 1.891726E+07, 1.963410E+07, 2.037217E+07, 2.113191E+07, 2.191375E+07,
2.271818E+07, 2.354568E+07, 2.439673E+07, 2.527179E+07, 2.617137E+07, 2.709593E+07,
2.804603E+07, 2.902211E+07, 3.002473E+07, 3.105443E+07, 3.211166E+07, 3.319703E+07,
3.431104E+07, 3.545426E+07, 3.662721E+07, 3.783046E+07, 3.906458E+07, 4.033016E+07,
4.162776E+07, 4.295797E+07, 4.432139E+07, 4.571860E+07, 4.715020E+07, 4.861682E+07,
5.011909E+07, 5.165761E+07, 5.323299E+07, 5.484596E+07, 5.649708E+07, 5.818704E+07,
5.991646E+07, 6.168607E+07, 6.349652E+07, 6.534847E+07, 6.724265E+07, 6.917967E+07,
7.116036E+07, 7.318530E+07, 7.525531E+07, 7.737105E+07, 7.953329E+07, 8.174279E+07,
8.400021E+07, 8.630635E+07, 8.866200E+07, 9.106790E+07, 9.352481E+07, 9.603355E+07,
9.859486E+07, 1.012096E+08, 1.038786E+08, 1.066026E+08, 1.093824E+08, 1.122189E+08,
1.151128E+08, 1.180652E+08, 1.210768E+08, 1.241484E+08, 1.272810E+08, 1.304754E+08,
1.337324E+08, 1.370531E+08, 1.404382E+08, 1.438888E+08, 1.474056E+08, 1.509897E+08,
1.546420E+08, 1.583634E+08, 1.621548E+08, 1.660172E+08, 1.699516E+08, 1.739590E+08,
1.780403E+08, 1.821965E+08,
])
# ---------------------- M = 3, I = 13 ---------------------------
M = 3
I = 13
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
9.181300E-01, 6.995503E+01, 1.971073E+02, 3.617463E+02, 5.567837E+02, 7.781615E+02,
1.023352E+03, 1.290876E+03, 1.580177E+03, 1.891452E+03, 2.225587E+03, 2.583933E+03,
2.968275E+03, 3.380727E+03, 3.823605E+03, 4.299458E+03, 4.810934E+03, 5.360874E+03,
5.952091E+03, 6.587671E+03, 7.270592E+03, 8.004158E+03, 8.791511E+03, 9.636056E+03,
1.054129E+04, 1.151069E+04, 1.254790E+04, 1.365681E+04, 1.484101E+04, 1.610476E+04,
1.745189E+04, 1.888674E+04, 2.041352E+04, 2.203664E+04, 2.376087E+04, 2.559056E+04,
2.753068E+04, 2.958620E+04, 3.176202E+04, 3.406331E+04, 3.649554E+04, 3.906410E+04,
4.177453E+04, 4.463243E+04, 4.764399E+04, 5.081487E+04, 5.415128E+04, 5.765957E+04,
6.134601E+04, 6.521766E+04, 6.928060E+04, 7.354192E+04, 7.800896E+04, 8.268836E+04,
8.758771E+04, 9.271449E+04, 9.807632E+04, 1.036807E+05, 1.095361E+05, 1.156503E+05,
1.220315E+05, 1.286881E+05, 1.356287E+05, 1.428623E+05, 1.503977E+05, 1.582439E+05,
1.664102E+05, 1.749058E+05, 1.837406E+05, 1.929240E+05, 2.024661E+05, 2.123774E+05,
2.226672E+05, 2.333470E+05, 2.444267E+05, 2.559177E+05, 2.678304E+05, 2.801759E+05,
2.929662E+05, 3.062125E+05, 3.199267E+05, 3.341200E+05, 3.488054E+05, 3.639945E+05,
3.797001E+05, 3.959348E+05, 4.127111E+05, 4.300426E+05, 4.479421E+05, 4.664231E+05,
4.854994E+05, 5.051847E+05, 5.254935E+05, 5.464391E+05, 5.680363E+05, 5.902997E+05,
6.132443E+05, 6.368844E+05, 6.612361E+05, 6.863149E+05, 7.121362E+05, 7.387153E+05,
7.660688E+05, 7.942129E+05, 8.231639E+05, 8.529386E+05, 8.835533E+05, 9.150268E+05,
9.473747E+05, 9.806152E+05, 1.014766E+06, 1.049845E+06, 1.085871E+06, 1.122862E+06,
1.160837E+06, 1.199813E+06, 1.239812E+06, 1.280852E+06, 1.322952E+06, 1.366132E+06,
1.410413E+06, 1.455814E+06, 1.502357E+06, 1.550060E+06, 1.598947E+06, 1.649037E+06,
1.700354E+06, 1.752916E+06, 1.806748E+06, 1.861869E+06, 1.918305E+06, 1.976076E+06,
2.035207E+06, 2.095720E+06, 2.157636E+06, 2.220983E+06, 2.285782E+06, 2.352058E+06,
2.419834E+06, 2.489135E+06, 2.559988E+06, 2.632416E+06, 2.706446E+06, 2.782102E+06,
2.859408E+06, 2.938395E+06, 3.019085E+06, 3.101507E+06, 3.185689E+06, 3.271654E+06,
3.359434E+06, 3.449053E+06, 3.540540E+06, 3.633925E+06, 3.729233E+06, 3.826498E+06,
3.925744E+06, 4.027002E+06, 4.130304E+06, 4.235674E+06, 4.343148E+06, 4.452752E+06,
4.564520E+06, 4.678482E+06, 4.794667E+06, 4.913111E+06, 5.033839E+06, 5.156890E+06,
5.282292E+06, 5.410079E+06, 5.540285E+06, 5.672941E+06, 5.808079E+06, 5.945739E+06,
6.085949E+06, 6.228746E+06,
])
# ---------------------- M = 3, I = 14 ---------------------------
M = 3
I = 14
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.070955E+01, 8.123365E+02, 2.288590E+03, 4.200027E+03, 6.464366E+03, 9.034498E+03,
1.188072E+04, 1.498574E+04, 1.834233E+04, 2.195242E+04, 2.582553E+04, 2.997699E+04,
3.442690E+04, 3.919918E+04, 4.432047E+04, 4.981933E+04, 5.572680E+04, 6.207426E+04,
6.889543E+04, 7.622379E+04, 8.409527E+04, 9.254631E+04, 1.016143E+05, 1.113367E+05,
1.217532E+05, 1.329047E+05, 1.448337E+05, 1.575806E+05, 1.711918E+05, 1.857116E+05,
2.011872E+05, 2.176646E+05, 2.351958E+05, 2.538270E+05, 2.736142E+05, 2.946102E+05,
3.168671E+05, 3.404431E+05, 3.653942E+05, 3.917795E+05, 4.196636E+05, 4.491018E+05,
4.801635E+05, 5.129110E+05, 5.474123E+05, 5.837346E+05, 6.219462E+05, 6.621222E+05,
7.043368E+05, 7.486602E+05, 7.951695E+05, 8.439459E+05, 8.950674E+05, 9.486189E+05,
1.004678E+06, 1.063332E+06, 1.124673E+06, 1.188782E+06, 1.255752E+06, 1.325673E+06,
1.398645E+06, 1.474761E+06, 1.554118E+06, 1.636818E+06, 1.722956E+06, 1.812642E+06,
1.905980E+06, 2.003073E+06, 2.104038E+06, 2.208977E+06, 2.318006E+06, 2.431244E+06,
2.548807E+06, 2.670808E+06, 2.797373E+06, 2.928627E+06, 3.064690E+06, 3.205688E+06,
3.351759E+06, 3.503023E+06, 3.659621E+06, 3.821690E+06, 3.989358E+06, 4.162770E+06,
4.342073E+06, 4.527402E+06, 4.718908E+06, 4.916745E+06, 5.121048E+06, 5.331982E+06,
5.549699E+06, 5.774354E+06, 6.006112E+06, 6.245127E+06, 6.491574E+06, 6.745604E+06,
7.007404E+06, 7.277128E+06, 7.554956E+06, 7.841067E+06, 8.135632E+06, 8.438833E+06,
8.750860E+06, 9.071888E+06, 9.402110E+06, 9.741713E+06, 1.009090E+07, 1.044984E+07,
1.081877E+07, 1.119784E+07, 1.158730E+07, 1.198732E+07, 1.239813E+07, 1.281992E+07,
1.325293E+07, 1.369734E+07, 1.415340E+07, 1.462130E+07, 1.510128E+07, 1.559357E+07,
1.609838E+07, 1.661596E+07, 1.714652E+07, 1.769032E+07, 1.824757E+07, 1.881854E+07,
1.940346E+07, 2.000258E+07, 2.061614E+07, 2.124441E+07, 2.188760E+07, 2.254603E+07,
2.321992E+07, 2.390954E+07, 2.461516E+07, 2.533705E+07, 2.607547E+07, 2.683070E+07,
2.760302E+07, 2.839271E+07, 2.920004E+07, 3.002532E+07, 3.086881E+07, 3.173082E+07,
3.261163E+07, 3.351154E+07, 3.443087E+07, 3.536991E+07, 3.632895E+07, 3.730831E+07,
3.830832E+07, 3.932926E+07, 4.037147E+07, 4.143527E+07, 4.252097E+07, 4.362893E+07,
4.475943E+07, 4.591285E+07, 4.708949E+07, 4.828972E+07, 4.951385E+07, 5.076226E+07,
5.203526E+07, 5.333325E+07, 5.465654E+07, 5.600550E+07, 5.738049E+07, 5.878190E+07,
6.021006E+07, 6.166536E+07, 6.314819E+07, 6.465888E+07, 6.619786E+07, 6.776550E+07,
6.936214E+07, 7.098826E+07,
])
# ---------------------- M = 3, I = 15 ---------------------------
M = 3
I = 15
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
5.394640E+00, 4.109442E+02, 1.157868E+03, 2.124994E+03, 3.270687E+03, 4.571107E+03,
6.011346E+03, 7.582597E+03, 9.281391E+03, 1.110871E+04, 1.306931E+04, 1.517082E+04,
1.742335E+04, 1.983895E+04, 2.243062E+04, 2.521306E+04, 2.820139E+04, 3.141182E+04,
3.486102E+04, 3.856575E+04, 4.254401E+04, 4.681405E+04, 5.139459E+04, 5.630527E+04,
6.156522E+04, 6.719486E+04, 7.321552E+04, 7.964854E+04, 8.651617E+04, 9.384115E+04,
1.016460E+05, 1.099563E+05, 1.187949E+05, 1.281892E+05, 1.381630E+05, 1.487449E+05,
1.599612E+05, 1.718402E+05, 1.844110E+05, 1.977042E+05, 2.117483E+05, 2.265752E+05,
2.422176E+05, 2.587079E+05, 2.760779E+05, 2.943649E+05, 3.136008E+05, 3.338232E+05,
3.550696E+05, 3.773763E+05, 4.007822E+05, 4.253252E+05, 4.510473E+05, 4.779882E+05,
5.061912E+05, 5.356961E+05, 5.665499E+05, 5.987935E+05, 6.324759E+05, 6.676398E+05,
7.043359E+05, 7.426104E+05, 7.825101E+05, 8.240889E+05, 8.673977E+05, 9.124835E+05,
9.594033E+05, 1.008211E+06, 1.058958E+06, 1.111704E+06, 1.166502E+06, 1.223411E+06,
1.282491E+06, 1.343799E+06, 1.407399E+06, 1.473350E+06, 1.541716E+06, 1.612559E+06,
1.685944E+06, 1.761942E+06, 1.840612E+06, 1.922026E+06, 2.006251E+06, 2.093359E+06,
2.183422E+06, 2.276511E+06, 2.372694E+06, 2.472055E+06, 2.574663E+06, 2.680597E+06,
2.789932E+06, 2.902747E+06, 3.019125E+06, 3.139149E+06, 3.262894E+06, 3.390446E+06,
3.521894E+06, 3.657318E+06, 3.796809E+06, 3.940448E+06, 4.088333E+06, 4.240552E+06,
4.397193E+06, 4.558348E+06, 4.724115E+06, 4.894587E+06, 5.069865E+06, 5.250039E+06,
5.435212E+06, 5.625483E+06, 5.820957E+06, 6.021730E+06, 6.227909E+06, 6.439603E+06,
6.656912E+06, 6.879946E+06, 7.108816E+06, 7.343626E+06, 7.584494E+06, 7.831525E+06,
8.084849E+06, 8.344561E+06, 8.610791E+06, 8.883657E+06, 9.163268E+06, 9.449758E+06,
9.743239E+06, 1.004384E+07, 1.035168E+07, 1.066689E+07, 1.098959E+07, 1.131992E+07,
1.165801E+07, 1.200399E+07, 1.235798E+07, 1.272013E+07, 1.309056E+07, 1.346943E+07,
1.385686E+07, 1.425299E+07, 1.465797E+07, 1.507195E+07, 1.549505E+07, 1.592745E+07,
1.636926E+07, 1.682065E+07, 1.728178E+07, 1.775276E+07, 1.823379E+07, 1.872501E+07,
1.922656E+07, 1.973862E+07, 2.026134E+07, 2.079487E+07, 2.133938E+07, 2.189504E+07,
2.246202E+07, 2.304046E+07, 2.363056E+07, 2.423247E+07, 2.484636E+07, 2.547242E+07,
2.611081E+07, 2.676171E+07, 2.742530E+07, 2.810175E+07, 2.879126E+07, 2.949400E+07,
3.021015E+07, 3.093989E+07, 3.168343E+07, 3.244094E+07, 3.321261E+07, 3.399866E+07,
3.479924E+07, 3.561458E+07,
])
# ---------------------- M = 3, I = 16 ---------------------------
M = 3
I = 16
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
6.295915E+01, 4.775780E+03, 1.345460E+04, 2.469181E+04, 3.800370E+04, 5.311278E+04,
6.984465E+04, 8.809583E+04, 1.078215E+05, 1.290311E+05, 1.517738E+05, 1.761371E+05,
2.022330E+05, 2.301970E+05, 2.601801E+05, 2.923500E+05, 3.268756E+05, 3.639441E+05,
4.037454E+05, 4.464741E+05, 4.923320E+05, 5.415271E+05, 5.942775E+05, 6.508028E+05,
7.113265E+05, 7.760820E+05, 8.453078E+05, 9.192467E+05, 9.981526E+05, 1.082292E+06,
1.171923E+06, 1.267315E+06, 1.368758E+06, 1.476543E+06, 1.590956E+06, 1.712308E+06,
1.840917E+06, 1.977087E+06, 2.121159E+06, 2.273473E+06, 2.434375E+06, 2.604205E+06,
2.783338E+06, 2.972142E+06, 3.171006E+06, 3.380315E+06, 3.600476E+06, 3.831875E+06,
4.074950E+06, 4.330119E+06, 4.597824E+06, 4.878515E+06, 5.172635E+06, 5.480655E+06,
5.803069E+06, 6.140331E+06, 6.492956E+06, 6.861430E+06, 7.246294E+06, 7.648073E+06,
8.067282E+06, 8.504471E+06, 8.960216E+06, 9.435065E+06, 9.929615E+06, 1.044443E+07,
1.098014E+07, 1.153735E+07, 1.211666E+07, 1.271871E+07, 1.334416E+07, 1.399362E+07,
1.466782E+07, 1.536741E+07, 1.609309E+07, 1.684551E+07, 1.762546E+07, 1.843360E+07,
1.927066E+07, 2.013746E+07, 2.103470E+07, 2.196320E+07, 2.292366E+07, 2.391697E+07,
2.494387E+07, 2.600520E+07, 2.710179E+07, 2.823452E+07, 2.940420E+07, 3.061173E+07,
3.185798E+07, 3.314380E+07, 3.447017E+07, 3.583801E+07, 3.724820E+07, 3.870172E+07,
4.019952E+07, 4.174256E+07, 4.333188E+07, 4.496840E+07, 4.665318E+07, 4.838728E+07,
5.017165E+07, 5.200740E+07, 5.389561E+07, 5.583735E+07, 5.783364E+07, 5.988569E+07,
6.199461E+07, 6.416146E+07, 6.638749E+07, 6.867377E+07, 7.102154E+07, 7.343199E+07,
7.590634E+07, 7.844571E+07, 8.105146E+07, 8.372481E+07, 8.646697E+07, 8.927926E+07,
9.216301E+07, 9.511942E+07, 9.814997E+07, 1.012559E+08, 1.044386E+08, 1.076993E+08,
1.110396E+08, 1.144608E+08, 1.179644E+08, 1.215516E+08, 1.252241E+08, 1.289833E+08,
1.328306E+08, 1.367675E+08, 1.407957E+08, 1.449164E+08, 1.491314E+08, 1.534422E+08,
1.578503E+08, 1.623573E+08, 1.669650E+08, 1.716746E+08, 1.764882E+08, 1.814073E+08,
1.864334E+08, 1.915684E+08, 1.968138E+08, 2.021716E+08, 2.076433E+08, 2.132308E+08,
2.189357E+08, 2.247600E+08, 2.307054E+08, 2.367737E+08, 2.429668E+08, 2.492865E+08,
2.557347E+08, 2.623134E+08, 2.690243E+08, 2.758695E+08, 2.828508E+08, 2.899701E+08,
2.972298E+08, 3.046315E+08, 3.121773E+08, 3.198694E+08, 3.277097E+08, 3.357002E+08,
3.438432E+08, 3.521405E+08, 3.605945E+08, 3.692073E+08, 3.779810E+08, 3.869178E+08,
3.960200E+08, 4.052896E+08,
])
# ---------------------- M = 3, I = 17 ---------------------------
M = 3
I = 17
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
3.126567E+01, 2.362010E+03, 6.653739E+03, 1.221051E+04, 1.879316E+04, 2.626447E+04,
3.453787E+04, 4.356149E+04, 5.331364E+04, 6.379765E+04, 7.503938E+04, 8.708147E+04,
9.998024E+04, 1.138041E+05, 1.286285E+05, 1.445357E+05, 1.616128E+05, 1.799519E+05,
1.996462E+05, 2.207944E+05, 2.434973E+05, 2.678588E+05, 2.939855E+05, 3.219866E+05,
3.519763E+05, 3.840710E+05, 4.183868E+05, 4.550447E+05, 4.941742E+05, 5.359053E+05,
5.803658E+05, 6.276941E+05, 6.780312E+05, 7.315213E+05, 7.883119E+05, 8.485581E+05,
9.124080E+05, 9.800237E+05, 1.051575E+06, 1.127220E+06, 1.207142E+06, 1.291512E+06,
1.380510E+06, 1.474325E+06, 1.573144E+06, 1.677168E+06, 1.786591E+06, 1.901612E+06,
2.022451E+06, 2.149307E+06, 2.282409E+06, 2.421973E+06, 2.568228E+06, 2.721418E+06,
2.881762E+06, 3.049503E+06, 3.224910E+06, 3.408205E+06, 3.599666E+06, 3.799557E+06,
4.008133E+06, 4.225669E+06, 4.452440E+06, 4.688742E+06, 4.934864E+06, 5.191084E+06,
5.457716E+06, 5.735050E+06, 6.023406E+06, 6.323116E+06, 6.634468E+06, 6.957803E+06,
7.293467E+06, 7.641780E+06, 8.003085E+06, 8.377742E+06, 8.766122E+06, 9.168553E+06,
9.585412E+06, 1.001709E+07, 1.046394E+07, 1.092639E+07, 1.140478E+07, 1.189953E+07,
1.241103E+07, 1.293971E+07, 1.348598E+07, 1.405025E+07, 1.463297E+07, 1.523455E+07,
1.585545E+07, 1.649609E+07, 1.715694E+07, 1.783848E+07, 1.854115E+07, 1.926541E+07,
2.001179E+07, 2.078071E+07, 2.157272E+07, 2.238827E+07, 2.322791E+07, 2.409212E+07,
2.498146E+07, 2.589638E+07, 2.683750E+07, 2.780528E+07, 2.880035E+07, 2.982319E+07,
3.087441E+07, 3.195455E+07, 3.306416E+07, 3.420390E+07, 3.537427E+07, 3.657594E+07,
3.780946E+07, 3.907546E+07, 4.037457E+07, 4.170740E+07, 4.307458E+07, 4.447674E+07,
4.591456E+07, 4.738867E+07, 4.889974E+07, 5.044841E+07, 5.203540E+07, 5.366135E+07,
5.532700E+07, 5.703306E+07, 5.878013E+07, 6.056905E+07, 6.240048E+07, 6.427519E+07,
6.619386E+07, 6.815728E+07, 7.016617E+07, 7.222130E+07, 7.432351E+07, 7.647348E+07,
7.867203E+07, 8.091997E+07, 8.321813E+07, 8.556722E+07, 8.796816E+07, 9.042171E+07,
9.292876E+07, 9.549011E+07, 9.810667E+07, 1.007792E+08, 1.035087E+08, 1.062959E+08,
1.091418E+08, 1.120472E+08, 1.150131E+08, 1.180403E+08, 1.211298E+08, 1.242826E+08,
1.274995E+08, 1.307815E+08, 1.341295E+08, 1.375446E+08, 1.410276E+08, 1.445796E+08,
1.482015E+08, 1.518944E+08, 1.556593E+08, 1.594971E+08, 1.634089E+08, 1.673958E+08,
1.714587E+08, 1.755987E+08, 1.798170E+08, 1.841145E+08, 1.884924E+08, 1.929516E+08,
1.974934E+08, 2.021189E+08,
])
# ---------------------- M = 3, I = 18 ---------------------------
M = 3
I = 18
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.837627E+02, 1.387967E+04, 3.909803E+04, 7.174973E+04, 1.104283E+05, 1.543309E+05,
2.029434E+05, 2.559580E+05, 3.132406E+05, 3.748050E+05, 4.407836E+05, 5.114192E+05,
5.870305E+05, 6.680003E+05, 7.547560E+05, 8.477726E+05, 9.475368E+05, 1.054582E+06,
1.169449E+06, 1.292692E+06, 1.424898E+06, 1.566662E+06, 1.718594E+06, 1.881319E+06,
2.055491E+06, 2.241760E+06, 2.440823E+06, 2.653357E+06, 2.880106E+06, 3.121812E+06,
3.379209E+06, 3.653089E+06, 3.944236E+06, 4.253526E+06, 4.581753E+06, 4.929803E+06,
5.298553E+06, 5.688930E+06, 6.101879E+06, 6.538337E+06, 6.999325E+06, 7.485814E+06,
7.998834E+06, 8.539473E+06, 9.108804E+06, 9.707966E+06, 1.033803E+07, 1.100023E+07,
1.169569E+07, 1.242568E+07, 1.319142E+07, 1.399419E+07, 1.483526E+07, 1.571596E+07,
1.663761E+07, 1.760170E+07, 1.860954E+07, 1.966258E+07, 2.076236E+07, 2.191031E+07,
2.310791E+07, 2.435683E+07, 2.565854E+07, 2.701471E+07, 2.842701E+07, 2.989710E+07,
3.142661E+07, 3.301744E+07, 3.467120E+07, 3.638967E+07, 3.817485E+07, 4.002844E+07,
4.195248E+07, 4.394874E+07, 4.601924E+07, 4.816607E+07, 5.039106E+07, 5.269646E+07,
5.508422E+07, 5.755659E+07, 6.011559E+07, 6.276353E+07, 6.550260E+07, 6.833496E+07,
7.126300E+07, 7.428912E+07, 7.741560E+07, 8.064479E+07, 8.397913E+07, 8.742121E+07,
9.097347E+07, 9.463835E+07, 9.841865E+07, 1.023169E+08, 1.063354E+08, 1.104775E+08,
1.147455E+08, 1.191421E+08, 1.236703E+08, 1.283330E+08, 1.331328E+08, 1.380728E+08,
1.431559E+08, 1.483852E+08, 1.537637E+08, 1.592942E+08, 1.649801E+08, 1.708245E+08,
1.768305E+08, 1.830015E+08, 1.893405E+08, 1.958510E+08, 2.025364E+08, 2.093999E+08,
2.164450E+08, 2.236751E+08, 2.310939E+08, 2.387049E+08, 2.465116E+08, 2.545176E+08,
2.627268E+08, 2.711426E+08, 2.797689E+08, 2.886098E+08, 2.976687E+08, 3.069497E+08,
3.164567E+08, 3.261937E+08, 3.361646E+08, 3.463737E+08, 3.568248E+08, 3.675224E+08,
3.784706E+08, 3.896733E+08, 4.011353E+08, 4.128607E+08, 4.248538E+08, 4.371189E+08,
4.496609E+08, 4.624839E+08, 4.755926E+08, 4.889916E+08, 5.026858E+08, 5.166797E+08,
5.309777E+08, 5.455850E+08, 5.605064E+08, 5.757467E+08, 5.913112E+08, 6.072043E+08,
6.234311E+08, 6.399973E+08, 6.569072E+08, 6.741666E+08, 6.917803E+08, 7.097538E+08,
7.280923E+08, 7.468014E+08, 7.658867E+08, 7.853528E+08, 8.052060E+08, 8.254517E+08,
8.460953E+08, 8.671429E+08, 8.885996E+08, 9.104718E+08, 9.327650E+08, 9.554851E+08,
9.786380E+08, 1.002230E+09, 1.026267E+09, 1.050754E+09, 1.075698E+09, 1.101107E+09,
1.126984E+09, 1.153337E+09,
])
# ---------------------- M = 4, I = 1 ---------------------------
M = 4
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[0]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.834054E+01, 3.016016E+02, 6.001924E+02, 8.988074E+02, 1.197498E+03, 1.496709E+03,
1.797823E+03, 2.103285E+03, 2.416237E+03, 2.740046E+03, 3.077980E+03, 3.433067E+03,
3.808070E+03, 4.205517E+03, 4.627758E+03, 5.077011E+03, 5.555411E+03, 6.065047E+03,
6.607987E+03, 7.186308E+03, 7.802105E+03, 8.457512E+03, 9.154706E+03, 9.895923E+03,
1.068346E+04, 1.151968E+04, 1.240701E+04, 1.334798E+04, 1.434516E+04, 1.540125E+04,
1.651898E+04, 1.770123E+04, 1.895091E+04, 2.027106E+04, 2.166480E+04, 2.313534E+04,
2.468602E+04, 2.632023E+04, 2.804150E+04, 2.985343E+04, 3.175977E+04, 3.376432E+04,
3.587102E+04, 3.808392E+04, 4.040717E+04, 4.284502E+04, 4.540186E+04, 4.808216E+04,
5.089054E+04, 5.383169E+04, 5.691045E+04, 6.013176E+04, 6.350068E+04, 6.702236E+04,
7.070211E+04, 7.454531E+04, 7.855746E+04, 8.274419E+04, 8.711122E+04, 9.166437E+04,
9.640958E+04, 1.013529E+05, 1.065004E+05, 1.118584E+05, 1.174331E+05, 1.232310E+05,
1.292585E+05, 1.355222E+05, 1.420287E+05, 1.487848E+05, 1.557971E+05, 1.630726E+05,
1.706179E+05, 1.784402E+05, 1.865463E+05, 1.949432E+05, 2.036378E+05, 2.126373E+05,
2.219487E+05, 2.315789E+05, 2.415351E+05, 2.518242E+05, 2.624534E+05, 2.734295E+05,
2.847595E+05, 2.964505E+05, 3.085092E+05, 3.209425E+05, 3.337573E+05, 3.469602E+05,
3.605579E+05, 3.745571E+05, 3.889642E+05, 4.037857E+05, 4.190280E+05, 4.346973E+05,
4.507998E+05, 4.673415E+05, 4.843284E+05, 5.017665E+05, 5.196613E+05, 5.380185E+05,
5.568437E+05, 5.761421E+05, 5.959191E+05, 6.161798E+05, 6.369291E+05, 6.581719E+05,
6.799128E+05, 7.021566E+05, 7.249075E+05, 7.481698E+05, 7.719478E+05, 7.962454E+05,
8.210663E+05, 8.464145E+05, 8.722932E+05, 8.987061E+05, 9.256562E+05, 9.531467E+05,
9.811806E+05, 1.009761E+06, 1.038889E+06, 1.068569E+06, 1.098803E+06, 1.129593E+06,
1.160940E+06, 1.192847E+06, 1.225316E+06, 1.258347E+06, 1.291944E+06, 1.326106E+06,
1.360835E+06, 1.396133E+06, 1.431999E+06, 1.468436E+06, 1.505443E+06, 1.543020E+06,
1.581169E+06, 1.619890E+06, 1.659183E+06, 1.699047E+06, 1.739482E+06, 1.780489E+06,
1.822067E+06, 1.864215E+06, 1.906934E+06, 1.950221E+06, 1.994077E+06, 2.038500E+06,
2.083489E+06, 2.129044E+06, 2.175162E+06, 2.221843E+06, 2.269086E+06, 2.316888E+06,
2.365247E+06, 2.414164E+06, 2.463635E+06, 2.513658E+06, 2.564232E+06, 2.615355E+06,
2.667024E+06, 2.719237E+06, 2.771992E+06, 2.825287E+06, 2.879119E+06, 2.933486E+06,
2.988385E+06, 3.043814E+06, 3.099769E+06, 3.156248E+06, 3.213249E+06, 3.270768E+06,
3.328803E+06, 3.387350E+06, 3.446406E+06, 3.505969E+06, 3.566036E+06, 3.626603E+06,
3.687666E+06, 3.749224E+06, 3.811272E+06, 3.873807E+06, 3.936826E+06, 4.000325E+06,
4.064302E+06, 4.128752E+06, 4.193672E+06, 4.259058E+06, 4.324908E+06, 4.391217E+06,
4.457982E+06, 4.525200E+06, 4.592866E+06, 4.660977E+06, 4.729530E+06, 4.798520E+06,
4.867944E+06, 4.937799E+06, 5.008080E+06, 5.078784E+06, 5.149907E+06, 5.221446E+06,
5.293396E+06, 5.365753E+06, 5.438515E+06, 5.511677E+06, 5.585236E+06, 5.659187E+06,
5.733527E+06, 5.808252E+06, 5.883358E+06, 5.958842E+06, 6.034699E+06, 6.110926E+06,
6.187520E+06, 6.264475E+06, 6.341789E+06, 6.419458E+06, 6.497478E+06, 6.575844E+06,
6.654554E+06, 6.733604E+06, 6.812990E+06, 6.892708E+06, 6.972754E+06, 7.053125E+06,
7.133816E+06, 7.214826E+06, 7.296148E+06, 7.377781E+06, 7.459719E+06, 7.541961E+06,
7.624501E+06, 7.707336E+06, 7.790463E+06, 7.873879E+06, 7.957578E+06, 8.041559E+06,
8.125817E+06, 8.210349E+06, 8.295151E+06, 8.380220E+06, 8.465552E+06, 8.551144E+06,
8.636993E+06, 8.723094E+06, 8.809445E+06, 8.896042E+06, 8.982882E+06,
])
# ---------------------- M = 4, I = 2 ---------------------------
M = 4
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.222856E+01, 2.011405E+02, 4.003536E+02, 5.996615E+02, 7.991092E+02, 9.990614E+02,
1.200554E+03, 1.405397E+03, 1.615831E+03, 1.834200E+03, 2.062784E+03, 2.303629E+03,
2.558624E+03, 2.829471E+03, 3.117766E+03, 3.425050E+03, 3.752753E+03, 4.102350E+03,
4.475234E+03, 4.872840E+03, 5.296650E+03, 5.748165E+03, 6.228913E+03, 6.740417E+03,
7.284339E+03, 7.862305E+03, 8.476054E+03, 9.127358E+03, 9.818007E+03, 1.054988E+04,
1.132500E+04, 1.214527E+04, 1.301286E+04, 1.392980E+04, 1.489831E+04, 1.592072E+04,
1.699933E+04, 1.813650E+04, 1.933465E+04, 2.059648E+04, 2.192444E+04, 2.332135E+04,
2.478986E+04, 2.633284E+04, 2.795313E+04, 2.965383E+04, 3.143800E+04, 3.330869E+04,
3.526918E+04, 3.732275E+04, 3.947275E+04, 4.172267E+04, 4.407610E+04, 4.653671E+04,
4.910823E+04, 5.179425E+04, 5.459898E+04, 5.752616E+04, 6.058007E+04, 6.376470E+04,
6.708449E+04, 7.054360E+04, 7.414663E+04, 7.789815E+04, 8.180281E+04, 8.586522E+04,
9.009033E+04, 9.448303E+04, 9.904846E+04, 1.037917E+05, 1.087180E+05, 1.138327E+05,
1.191414E+05, 1.246497E+05, 1.303630E+05, 1.362872E+05, 1.424284E+05, 1.487924E+05,
1.553853E+05, 1.622135E+05, 1.692834E+05, 1.766012E+05, 1.841737E+05, 1.920075E+05,
2.001097E+05, 2.084867E+05, 2.171461E+05, 2.260948E+05, 2.353401E+05, 2.448897E+05,
2.547509E+05, 2.649314E+05, 2.754390E+05, 2.862820E+05, 2.974681E+05, 3.090057E+05,
3.209029E+05, 3.331687E+05, 3.458111E+05, 3.588390E+05, 3.722616E+05, 3.860877E+05,
4.003264E+05, 4.149873E+05, 4.300793E+05, 4.456126E+05, 4.615966E+05, 4.780413E+05,
4.949566E+05, 5.123527E+05, 5.302399E+05, 5.486289E+05, 5.675301E+05, 5.869545E+05,
6.069124E+05, 6.274157E+05, 6.484752E+05, 6.701022E+05, 6.923088E+05, 7.151059E+05,
7.385061E+05, 7.625212E+05, 7.871631E+05, 8.124445E+05, 8.383776E+05, 8.649755E+05,
8.922511E+05, 9.202168E+05, 9.488864E+05, 9.782731E+05, 1.008390E+06, 1.039252E+06,
1.070871E+06, 1.103263E+06, 1.136442E+06, 1.170421E+06, 1.205216E+06, 1.240841E+06,
1.277312E+06, 1.314642E+06, 1.352849E+06, 1.391947E+06, 1.431951E+06, 1.472878E+06,
1.514745E+06, 1.557566E+06, 1.601359E+06, 1.646140E+06, 1.691926E+06, 1.738734E+06,
1.786582E+06, 1.835486E+06, 1.885465E+06, 1.936536E+06, 1.988717E+06, 2.042026E+06,
2.096482E+06, 2.152104E+06, 2.208910E+06, 2.266919E+06, 2.326151E+06, 2.386625E+06,
2.448361E+06, 2.511378E+06, 2.575697E+06, 2.641338E+06, 2.708322E+06, 2.776668E+06,
2.846400E+06, 2.917536E+06, 2.990100E+06, 3.064111E+06, 3.139593E+06, 3.216567E+06,
3.295056E+06, 3.375082E+06,
])
# ---------------------- M = 4, I = 3 ---------------------------
M = 4
I = 3
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.256526E+01, 2.080869E+02, 4.142500E+02, 6.205111E+02, 8.269083E+02, 1.033772E+03,
1.242093E+03, 1.453602E+03, 1.670525E+03, 1.895214E+03, 2.129981E+03, 2.376914E+03,
2.637982E+03, 2.914901E+03, 3.209368E+03, 3.522887E+03, 3.857001E+03, 4.213185E+03,
4.592889E+03, 4.997581E+03, 5.428733E+03, 5.887874E+03, 6.376560E+03, 6.896343E+03,
7.448865E+03, 8.035829E+03, 8.658960E+03, 9.319999E+03, 1.002082E+04, 1.076333E+04,
1.154942E+04, 1.238119E+04, 1.326072E+04, 1.419008E+04, 1.517143E+04, 1.620721E+04,
1.729969E+04, 1.845127E+04, 1.966438E+04, 2.094161E+04, 2.228561E+04, 2.369906E+04,
2.518473E+04, 2.674545E+04, 2.838410E+04, 3.010380E+04, 3.190749E+04, 3.379832E+04,
3.577964E+04, 3.785476E+04, 4.002697E+04, 4.229982E+04, 4.467683E+04, 4.716170E+04,
4.975823E+04, 5.247004E+04, 5.530117E+04, 5.825564E+04, 6.133755E+04, 6.455106E+04,
6.790052E+04, 7.139005E+04, 7.502442E+04, 7.880813E+04, 8.274562E+04, 8.684195E+04,
9.110174E+04, 9.553009E+04, 1.001320E+05, 1.049128E+05, 1.098775E+05, 1.150316E+05,
1.203805E+05, 1.259299E+05, 1.316857E+05, 1.376532E+05, 1.438385E+05, 1.502479E+05,
1.568873E+05, 1.637628E+05, 1.708812E+05, 1.782486E+05, 1.858719E+05, 1.937574E+05,
2.019124E+05, 2.103435E+05, 2.190582E+05, 2.280632E+05, 2.373661E+05, 2.469743E+05,
2.568955E+05, 2.671372E+05, 2.777073E+05, 2.886138E+05, 2.998648E+05, 3.114686E+05,
3.234334E+05, 3.357677E+05, 3.484802E+05, 3.615796E+05, 3.750749E+05, 3.889752E+05,
4.032894E+05, 4.180269E+05, 4.331973E+05, 4.488102E+05, 4.648751E+05, 4.814022E+05,
4.984014E+05, 5.158830E+05, 5.338571E+05, 5.523341E+05, 5.713253E+05, 5.908406E+05,
6.108916E+05, 6.314891E+05, 6.526444E+05, 6.743689E+05, 6.966742E+05, 7.195719E+05,
7.430743E+05, 7.671928E+05, 7.919402E+05, 8.173284E+05, 8.433701E+05, 8.700783E+05,
8.974653E+05, 9.255449E+05, 9.543294E+05, 9.838329E+05, 1.014069E+06, 1.045050E+06,
1.076792E+06, 1.109308E+06, 1.142611E+06, 1.176718E+06, 1.211642E+06, 1.247397E+06,
1.284000E+06, 1.321465E+06, 1.359807E+06, 1.399043E+06, 1.439187E+06, 1.480256E+06,
1.522265E+06, 1.565232E+06, 1.609171E+06, 1.654101E+06, 1.700038E+06, 1.746998E+06,
1.795000E+06, 1.844060E+06, 1.894197E+06, 1.945428E+06, 1.997770E+06, 2.051243E+06,
2.105865E+06, 2.161654E+06, 2.218630E+06, 2.276810E+06, 2.336216E+06, 2.396864E+06,
2.458777E+06, 2.521974E+06, 2.586474E+06, 2.652298E+06, 2.719466E+06, 2.788000E+06,
2.857920E+06, 2.929247E+06, 3.002003E+06, 3.076209E+06, 3.151888E+06, 3.229061E+06,
3.307750E+06, 3.387980E+06,
])
# ---------------------- M = 4, I = 4 ---------------------------
M = 4
I = 4
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.920009E+01, 3.193816E+02, 6.358818E+02, 9.525320E+02, 1.269392E+03, 1.586983E+03,
1.906819E+03, 2.231621E+03, 2.564811E+03, 2.910018E+03, 3.270780E+03, 3.650417E+03,
4.051873E+03, 4.477876E+03, 4.931017E+03, 5.413766E+03, 5.928387E+03, 6.477164E+03,
7.062454E+03, 7.686493E+03, 8.351633E+03, 9.060190E+03, 9.814583E+03, 1.061722E+04,
1.147081E+04, 1.237778E+04, 1.334087E+04, 1.436287E+04, 1.544662E+04, 1.659506E+04,
1.781131E+04, 1.909836E+04, 2.045953E+04, 2.189821E+04, 2.341770E+04, 2.502151E+04,
2.671353E+04, 2.849723E+04, 3.037651E+04, 3.235551E+04, 3.443819E+04, 3.662862E+04,
3.893132E+04, 4.135055E+04, 4.389090E+04, 4.655709E+04, 4.935379E+04, 5.228607E+04,
5.535866E+04, 5.857697E+04, 6.194628E+04, 6.547190E+04, 6.915948E+04, 7.301461E+04,
7.704295E+04, 8.125083E+04, 8.564401E+04, 9.022885E+04, 9.501173E+04, 9.999909E+04,
1.051977E+05, 1.106143E+05, 1.162557E+05, 1.221292E+05, 1.282420E+05, 1.346015E+05,
1.412153E+05, 1.480910E+05, 1.552365E+05, 1.626599E+05, 1.703693E+05, 1.783731E+05,
1.866800E+05, 1.952985E+05, 2.042375E+05, 2.135062E+05, 2.231135E+05, 2.330688E+05,
2.433819E+05, 2.540625E+05, 2.651203E+05, 2.765653E+05, 2.884082E+05, 3.006592E+05,
3.133289E+05, 3.264282E+05, 3.399683E+05, 3.539598E+05, 3.684149E+05, 3.833445E+05,
3.987610E+05, 4.146759E+05, 4.311017E+05, 4.480505E+05, 4.655352E+05, 4.835685E+05,
5.021632E+05, 5.213328E+05, 5.410906E+05, 5.614503E+05, 5.824256E+05, 6.040307E+05,
6.262797E+05, 6.491874E+05, 6.727680E+05, 6.970374E+05, 7.220099E+05, 7.477012E+05,
7.741267E+05, 8.013024E+05, 8.292446E+05, 8.579696E+05, 8.874938E+05, 9.178335E+05,
9.490066E+05, 9.810301E+05, 1.013921E+06, 1.047698E+06, 1.082378E+06, 1.117980E+06,
1.154522E+06, 1.192024E+06, 1.230503E+06, 1.269980E+06, 1.310473E+06, 1.352004E+06,
1.394590E+06, 1.438254E+06, 1.483015E+06, 1.528894E+06, 1.575913E+06, 1.624092E+06,
1.673454E+06, 1.724020E+06, 1.775813E+06, 1.828855E+06, 1.883168E+06, 1.938776E+06,
1.995702E+06, 2.053969E+06, 2.113602E+06, 2.174624E+06, 2.237060E+06, 2.300936E+06,
2.366274E+06, 2.433103E+06, 2.501445E+06, 2.571329E+06, 2.642780E+06, 2.715823E+06,
2.790487E+06, 2.866799E+06, 2.944785E+06, 3.024473E+06, 3.105892E+06, 3.189071E+06,
3.274037E+06, 3.360820E+06, 3.449449E+06, 3.539953E+06, 3.632364E+06, 3.726710E+06,
3.823023E+06, 3.921334E+06, 4.021673E+06, 4.124073E+06, 4.228565E+06, 4.335183E+06,
4.443957E+06, 4.554922E+06, 4.668111E+06, 4.783556E+06, 4.901294E+06, 5.021357E+06,
5.143780E+06, 5.268599E+06,
])
# ---------------------- M = 4, I = 5 ---------------------------
M = 4
I = 5
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.126859E+02, 1.864509E+03, 3.711701E+03, 5.559770E+03, 7.409060E+03, 9.262436E+03,
1.112884E+04, 1.302350E+04, 1.496642E+04, 1.697860E+04, 1.908053E+04, 2.129100E+04,
2.362782E+04, 2.610612E+04, 2.874132E+04, 3.154718E+04, 3.453696E+04, 3.772415E+04,
4.112140E+04, 4.474253E+04, 4.860004E+04, 5.270834E+04, 5.708061E+04, 6.173070E+04,
6.667417E+04, 7.192540E+04, 7.749930E+04, 8.341287E+04, 8.968139E+04, 9.632295E+04,
1.033539E+05, 1.107929E+05, 1.186581E+05, 1.269691E+05, 1.357447E+05, 1.450058E+05,
1.547736E+05, 1.650686E+05, 1.759141E+05, 1.873318E+05, 1.993451E+05, 2.119782E+05,
2.252566E+05, 2.392045E+05, 2.538486E+05, 2.692152E+05, 2.853310E+05, 3.022263E+05,
3.199273E+05, 3.384653E+05, 3.578704E+05, 3.781728E+05, 3.994046E+05, 4.215990E+05,
4.447877E+05, 4.690061E+05, 4.942895E+05, 5.206713E+05, 5.481903E+05, 5.768826E+05,
6.067872E+05, 6.379417E+05, 6.703877E+05, 7.041640E+05, 7.393128E+05, 7.758767E+05,
8.138982E+05, 8.534227E+05, 8.944954E+05, 9.371609E+05, 9.814658E+05, 1.027460E+06,
1.075192E+06, 1.124709E+06, 1.176064E+06, 1.229308E+06, 1.284495E+06, 1.341676E+06,
1.400908E+06, 1.462245E+06, 1.525743E+06, 1.591463E+06, 1.659462E+06, 1.729799E+06,
1.802536E+06, 1.877734E+06, 1.955456E+06, 2.035768E+06, 2.118733E+06, 2.204417E+06,
2.292890E+06, 2.384219E+06, 2.478473E+06, 2.575724E+06, 2.676044E+06, 2.779507E+06,
2.886184E+06, 2.996153E+06, 3.109493E+06, 3.226277E+06, 3.346586E+06, 3.470502E+06,
3.598108E+06, 3.729483E+06, 3.864712E+06, 4.003881E+06, 4.147080E+06, 4.294392E+06,
4.445907E+06, 4.601719E+06, 4.761916E+06, 4.926595E+06, 5.095849E+06, 5.269773E+06,
5.448463E+06, 5.632024E+06, 5.820548E+06, 6.014144E+06, 6.212908E+06, 6.416949E+06,
6.626371E+06, 6.841280E+06, 7.061789E+06, 7.288003E+06, 7.520035E+06, 7.758000E+06,
8.002011E+06, 8.252184E+06, 8.508638E+06, 8.771489E+06, 9.040859E+06, 9.316869E+06,
9.599643E+06, 9.889311E+06, 1.018599E+07, 1.048982E+07, 1.080092E+07, 1.111943E+07,
1.144548E+07, 1.177920E+07, 1.212074E+07, 1.247022E+07, 1.282778E+07, 1.319359E+07,
1.356776E+07, 1.395045E+07, 1.434180E+07, 1.474197E+07, 1.515110E+07, 1.556933E+07,
1.599684E+07, 1.643377E+07, 1.688027E+07, 1.733652E+07, 1.780266E+07, 1.827886E+07,
1.876528E+07, 1.926210E+07, 1.976946E+07, 2.028756E+07, 2.081655E+07, 2.135661E+07,
2.190792E+07, 2.247066E+07, 2.304498E+07, 2.363109E+07, 2.422917E+07, 2.483940E+07,
2.546196E+07, 2.609704E+07, 2.674484E+07, 2.740554E+07, 2.807935E+07, 2.876645E+07,
2.946704E+07, 3.018133E+07,
])
# ---------------------- M = 5, I = 1 ---------------------------
M = 5
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[3]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.011870E+00, 7.573520E+00, 1.480009E+01, 2.203056E+01, 2.926249E+01, 3.649540E+01,
4.372913E+01, 5.096362E+01, 5.819883E+01, 6.543475E+01, 7.267137E+01, 7.990872E+01,
8.714687E+01, 9.438597E+01, 1.016263E+02, 1.088684E+02, 1.161130E+02, 1.233610E+02,
1.306138E+02, 1.378730E+02, 1.451405E+02, 1.524185E+02, 1.597093E+02, 1.670157E+02,
1.743404E+02, 1.816863E+02, 1.890565E+02, 1.964539E+02, 2.038817E+02, 2.113428E+02,
2.188405E+02, 2.263775E+02, 2.339567E+02, 2.415811E+02, 2.492532E+02, 2.569757E+02,
2.647511E+02, 2.725817E+02, 2.804698E+02, 2.884176E+02, 2.964271E+02, 3.045004E+02,
3.126391E+02, 3.208450E+02, 3.291199E+02, 3.374652E+02, 3.458824E+02, 3.543729E+02,
3.629381E+02, 3.715790E+02, 3.802970E+02, 3.890930E+02, 3.979682E+02, 4.069234E+02,
4.159597E+02, 4.250779E+02, 4.342787E+02, 4.435630E+02, 4.529316E+02, 4.623850E+02,
4.719240E+02, 4.815492E+02, 4.912611E+02, 5.010603E+02, 5.109473E+02, 5.209227E+02,
5.309869E+02, 5.411404E+02, 5.513836E+02, 5.617168E+02, 5.721405E+02, 5.826552E+02,
5.932609E+02, 6.039583E+02, 6.147475E+02, 6.256289E+02, 6.366028E+02, 6.476694E+02,
6.588291E+02, 6.700820E+02, 6.814285E+02, 6.928687E+02, 7.044030E+02, 7.160315E+02,
7.277544E+02, 7.395719E+02, 7.514843E+02, 7.634917E+02, 7.755944E+02, 7.877924E+02,
8.000860E+02, 8.124754E+02, 8.249606E+02, 8.375419E+02, 8.502194E+02, 8.629933E+02,
8.758637E+02, 8.888307E+02, 9.018945E+02, 9.150553E+02, 9.283131E+02, 9.416680E+02,
9.551203E+02, 9.686700E+02, 9.823173E+02, 9.960622E+02, 1.009905E+03, 1.023845E+03,
1.037884E+03, 1.052021E+03, 1.066256E+03, 1.080589E+03, 1.095020E+03, 1.109551E+03,
1.124179E+03, 1.138907E+03, 1.153733E+03, 1.168658E+03, 1.183682E+03, 1.198806E+03,
1.214028E+03, 1.229350E+03, 1.244771E+03, 1.260292E+03, 1.275912E+03, 1.291632E+03,
1.307451E+03, 1.323370E+03, 1.339390E+03, 1.355509E+03, 1.371728E+03, 1.388047E+03,
1.404467E+03, 1.420987E+03, 1.437607E+03, 1.454327E+03, 1.471149E+03, 1.488070E+03,
1.505093E+03, 1.522216E+03, 1.539440E+03, 1.556765E+03, 1.574191E+03, 1.591718E+03,
1.609346E+03, 1.627076E+03, 1.644907E+03, 1.662839E+03, 1.680872E+03, 1.699007E+03,
1.717244E+03, 1.735582E+03, 1.754022E+03, 1.772564E+03, 1.791208E+03, 1.809954E+03,
1.828801E+03, 1.847751E+03, 1.866803E+03, 1.885957E+03, 1.905214E+03, 1.924573E+03,
1.944034E+03, 1.963598E+03, 1.983264E+03, 2.003034E+03, 2.022906E+03, 2.042880E+03,
2.062958E+03, 2.083138E+03, 2.103422E+03, 2.123809E+03, 2.144299E+03, 2.164892E+03,
2.185588E+03, 2.206388E+03, 2.227291E+03, 2.248298E+03, 2.269408E+03, 2.290622E+03,
2.311939E+03, 2.333361E+03, 2.354886E+03, 2.376515E+03, 2.398249E+03, 2.420086E+03,
2.442027E+03, 2.464073E+03, 2.486223E+03, 2.508477E+03, 2.530836E+03, 2.553299E+03,
2.575867E+03, 2.598539E+03, 2.621316E+03, 2.644198E+03, 2.667185E+03, 2.690276E+03,
2.713473E+03, 2.736775E+03, 2.760181E+03, 2.783693E+03, 2.807310E+03, 2.831033E+03,
2.854861E+03, 2.878794E+03, 2.902833E+03, 2.926977E+03, 2.951227E+03, 2.975583E+03,
3.000045E+03, 3.024612E+03, 3.049286E+03, 3.074065E+03, 3.098951E+03, 3.123943E+03,
3.149041E+03, 3.174245E+03, 3.199555E+03, 3.224973E+03, 3.250496E+03, 3.276126E+03,
3.301863E+03, 3.327706E+03, 3.353657E+03, 3.379714E+03, 3.405878E+03, 3.432149E+03,
3.458527E+03, 3.485012E+03, 3.511605E+03, 3.538304E+03, 3.565111E+03, 3.592026E+03,
3.619048E+03, 3.646177E+03, 3.673414E+03, 3.700759E+03, 3.728212E+03, 3.755772E+03,
3.783440E+03, 3.811216E+03, 3.839101E+03, 3.867093E+03, 3.895193E+03, 3.923402E+03,
3.951719E+03, 3.980145E+03, 4.008679E+03, 4.037321E+03, 4.066072E+03, 4.094932E+03,
4.123900E+03, 4.152977E+03, 4.182163E+03, 4.211458E+03, 4.240862E+03, 4.270375E+03,
4.299997E+03, 4.329729E+03, 4.359569E+03, 4.389519E+03, 4.419579E+03, 4.449747E+03,
4.480026E+03, 4.510414E+03, 4.540912E+03, 4.571519E+03, 4.602236E+03, 4.633064E+03,
4.664001E+03, 4.695048E+03, 4.726205E+03, 4.757472E+03, 4.788850E+03, 4.820338E+03,
4.851936E+03, 4.883645E+03, 4.915464E+03, 4.947394E+03, 4.979434E+03, 5.011585E+03,
5.043847E+03, 5.076220E+03, 5.108703E+03, 5.141298E+03, 5.174003E+03, 5.206820E+03,
5.239748E+03, 5.272787E+03, 5.305937E+03, 5.339199E+03, 5.372572E+03, 5.406056E+03,
5.439652E+03, 5.473360E+03, 5.507179E+03, 5.541110E+03, 5.575153E+03, 5.609308E+03,
5.643574E+03, 5.677953E+03, 5.712444E+03, 5.747047E+03, 5.781762E+03, 5.816589E+03,
5.851528E+03, 5.886580E+03, 5.921745E+03, 5.957021E+03, 5.992411E+03, 6.027913E+03,
6.063527E+03, 6.099255E+03, 6.135095E+03, 6.171048E+03, 6.207114E+03, 6.243293E+03,
6.279585E+03, 6.315990E+03, 6.352508E+03, 6.389140E+03, 6.425885E+03, 6.462743E+03,
6.499714E+03, 6.536799E+03, 6.573998E+03, 6.611310E+03, 6.648735E+03, 6.686275E+03,
6.723928E+03, 6.761694E+03, 6.799575E+03, 6.837570E+03, 6.875678E+03, 6.913901E+03,
6.952238E+03, 6.990688E+03, 7.029253E+03, 7.067933E+03, 7.106726E+03, 7.145634E+03,
7.184656E+03, 7.223793E+03, 7.263044E+03, 7.302410E+03, 7.341891E+03, 7.381486E+03,
7.421195E+03, 7.461020E+03, 7.500959E+03, 7.541014E+03, 7.581183E+03, 7.621467E+03,
7.661866E+03, 7.702380E+03, 7.743010E+03, 7.783754E+03, 7.824614E+03, 7.865589E+03,
7.906680E+03, 7.947885E+03, 7.989206E+03, 8.030643E+03, 8.072195E+03, 8.113863E+03,
8.155646E+03, 8.197545E+03, 8.239560E+03, 8.281690E+03, 8.323936E+03, 8.366298E+03,
8.408776E+03, 8.451369E+03, 8.494079E+03, 8.536905E+03, 8.579847E+03, 8.622904E+03,
8.666078E+03, 8.709368E+03, 8.752775E+03, 8.796297E+03, 8.839936E+03, 8.883691E+03,
8.927563E+03, 8.971551E+03, 9.015655E+03, 9.059876E+03, 9.104213E+03, 9.148667E+03,
9.193238E+03, 9.237925E+03, 9.282729E+03, 9.327650E+03, 9.372687E+03, 9.417841E+03,
9.463112E+03, 9.508500E+03, 9.554005E+03, 9.599626E+03, 9.645365E+03, 9.691221E+03,
9.737193E+03, 9.783283E+03, 9.829489E+03, 9.875813E+03, 9.922254E+03, 9.968812E+03,
1.001549E+04, 1.006228E+04, 1.010919E+04, 1.015622E+04, 1.020336E+04, 1.025062E+04,
1.029800E+04, 1.034550E+04, 1.039311E+04, 1.044085E+04, 1.048869E+04, 1.053666E+04,
1.058475E+04, 1.063295E+04, 1.068127E+04, 1.072970E+04, 1.077826E+04, 1.082693E+04,
1.087572E+04, 1.092463E+04, 1.097365E+04, 1.102280E+04, 1.107206E+04, 1.112144E+04,
1.117093E+04, 1.122055E+04, 1.127028E+04, 1.132013E+04, 1.137010E+04, 1.142018E+04,
1.147039E+04, 1.152071E+04, 1.157115E+04, 1.162170E+04, 1.167238E+04, 1.172317E+04,
1.177409E+04, 1.182512E+04, 1.187626E+04, 1.192753E+04, 1.197891E+04, 1.203042E+04,
1.208204E+04,
])
# ---------------------- M = 5, I = 2 ---------------------------
M = 5
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[3]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
2.030290E+00, 1.581148E+01, 3.093044E+01, 4.605695E+01, 6.118637E+01, 7.631778E+01,
9.145088E+01, 1.065855E+02, 1.217217E+02, 1.368594E+02, 1.519985E+02, 1.671392E+02,
1.822816E+02, 1.974261E+02, 2.125736E+02, 2.277251E+02, 2.428826E+02, 2.580484E+02,
2.732257E+02, 2.884181E+02, 3.036302E+02, 3.188669E+02, 3.341338E+02, 3.494367E+02,
3.647819E+02, 3.801760E+02, 3.956256E+02, 4.111372E+02, 4.267178E+02, 4.423738E+02,
4.581119E+02, 4.739383E+02, 4.898593E+02, 5.058809E+02, 5.220087E+02, 5.382483E+02,
5.546050E+02, 5.710838E+02, 5.876893E+02, 6.044262E+02, 6.212987E+02, 6.383109E+02,
6.554665E+02, 6.727693E+02, 6.902226E+02, 7.078295E+02, 7.255932E+02, 7.435165E+02,
7.616020E+02, 7.798524E+02, 7.982699E+02, 8.168568E+02, 8.356152E+02, 8.545471E+02,
8.736544E+02, 8.929388E+02, 9.124020E+02, 9.320457E+02, 9.518712E+02, 9.718800E+02,
9.920733E+02, 1.012453E+03, 1.033019E+03, 1.053773E+03, 1.074717E+03, 1.095851E+03,
1.117176E+03, 1.138693E+03, 1.160404E+03, 1.182308E+03, 1.204406E+03, 1.226700E+03,
1.249191E+03, 1.271877E+03, 1.294762E+03, 1.317844E+03, 1.341125E+03, 1.364605E+03,
1.388285E+03, 1.412165E+03, 1.436246E+03, 1.460528E+03, 1.485012E+03, 1.509698E+03,
1.534586E+03, 1.559678E+03, 1.584973E+03, 1.610472E+03, 1.636175E+03, 1.662083E+03,
1.688195E+03, 1.714513E+03, 1.741037E+03, 1.767766E+03, 1.794701E+03, 1.821843E+03,
1.849192E+03, 1.876748E+03, 1.904512E+03, 1.932483E+03, 1.960662E+03, 1.989049E+03,
2.017644E+03, 2.046449E+03, 2.075462E+03, 2.104685E+03, 2.134116E+03, 2.163758E+03,
2.193609E+03, 2.223671E+03, 2.253943E+03, 2.284425E+03, 2.315119E+03, 2.346023E+03,
2.377138E+03, 2.408465E+03, 2.440003E+03, 2.471753E+03, 2.503715E+03, 2.535889E+03,
2.568276E+03, 2.600875E+03, 2.633687E+03, 2.666711E+03, 2.699949E+03, 2.733400E+03,
2.767065E+03, 2.800943E+03, 2.835035E+03, 2.869341E+03, 2.903861E+03, 2.938595E+03,
2.973544E+03, 3.008708E+03, 3.044086E+03, 3.079680E+03, 3.115488E+03, 3.151512E+03,
3.187752E+03, 3.224207E+03, 3.260878E+03, 3.297764E+03, 3.334867E+03, 3.372187E+03,
3.409722E+03, 3.447475E+03, 3.485444E+03, 3.523630E+03, 3.562033E+03, 3.600653E+03,
3.639491E+03, 3.678547E+03, 3.717820E+03, 3.757310E+03, 3.797019E+03, 3.836946E+03,
3.877092E+03, 3.917456E+03, 3.958038E+03, 3.998839E+03, 4.039860E+03, 4.081099E+03,
4.122557E+03, 4.164235E+03, 4.206133E+03, 4.248250E+03, 4.290587E+03, 4.333143E+03,
4.375921E+03, 4.418918E+03, 4.462136E+03, 4.505574E+03, 4.549233E+03, 4.593113E+03,
4.637214E+03, 4.681536E+03, 4.726079E+03, 4.770844E+03, 4.815831E+03, 4.861039E+03,
4.906469E+03, 4.952121E+03, 4.997996E+03, 5.044093E+03, 5.090412E+03, 5.136954E+03,
5.183719E+03, 5.230707E+03, 5.277917E+03, 5.325351E+03, 5.373009E+03, 5.420890E+03,
5.468995E+03, 5.517323E+03, 5.565876E+03, 5.614652E+03, 5.663653E+03, 5.712879E+03,
5.762329E+03, 5.812003E+03, 5.861903E+03, 5.912027E+03, 5.962377E+03, 6.012952E+03,
6.063752E+03, 6.114778E+03, 6.166030E+03, 6.217508E+03, 6.269212E+03, 6.321142E+03,
6.373298E+03, 6.425681E+03, 6.478290E+03, 6.531126E+03, 6.584189E+03, 6.637479E+03,
6.690997E+03, 6.744742E+03, 6.798714E+03, 6.852914E+03, 6.907342E+03, 6.961997E+03,
7.016881E+03, 7.071993E+03, 7.127334E+03, 7.182903E+03, 7.238700E+03, 7.294727E+03,
7.350982E+03, 7.407467E+03, 7.464181E+03, 7.521124E+03, 7.578297E+03, 7.635699E+03,
7.693332E+03, 7.751194E+03, 7.809287E+03, 7.867610E+03, 7.926163E+03, 7.984947E+03,
8.043962E+03, 8.103207E+03, 8.162684E+03, 8.222391E+03, 8.282330E+03, 8.342501E+03,
8.402903E+03, 8.463537E+03, 8.524402E+03, 8.585500E+03, 8.646830E+03, 8.708392E+03,
8.770187E+03, 8.832214E+03, 8.894474E+03, 8.956967E+03, 9.019693E+03, 9.082652E+03,
9.145844E+03, 9.209270E+03, 9.272930E+03, 9.336823E+03, 9.400950E+03, 9.465311E+03,
9.529906E+03, 9.594736E+03, 9.659800E+03, 9.725098E+03, 9.790631E+03, 9.856400E+03,
9.922403E+03, 9.988641E+03, 1.005511E+04, 1.012182E+04, 1.018877E+04, 1.025595E+04,
1.032336E+04, 1.039102E+04, 1.045890E+04, 1.052703E+04, 1.059539E+04, 1.066399E+04,
1.073282E+04, 1.080189E+04, 1.087120E+04, 1.094074E+04, 1.101052E+04, 1.108054E+04,
1.115080E+04, 1.122129E+04, 1.129202E+04, 1.136299E+04, 1.143420E+04, 1.150565E+04,
1.157733E+04, 1.164925E+04, 1.172141E+04, 1.179381E+04, 1.186645E+04, 1.193932E+04,
1.201244E+04, 1.208579E+04, 1.215939E+04, 1.223322E+04, 1.230729E+04, 1.238161E+04,
1.245616E+04, 1.253095E+04, 1.260598E+04, 1.268125E+04, 1.275676E+04, 1.283252E+04,
1.290851E+04, 1.298474E+04, 1.306122E+04, 1.313793E+04, 1.321489E+04, 1.329208E+04,
1.336952E+04, 1.344720E+04, 1.352512E+04, 1.360328E+04, 1.368169E+04, 1.376033E+04,
1.383922E+04, 1.391835E+04, 1.399772E+04, 1.407733E+04, 1.415719E+04, 1.423729E+04,
1.431763E+04, 1.439821E+04, 1.447903E+04, 1.456010E+04, 1.464141E+04, 1.472297E+04,
1.480477E+04, 1.488681E+04, 1.496909E+04, 1.505162E+04, 1.513439E+04, 1.521740E+04,
1.530066E+04, 1.538416E+04, 1.546791E+04, 1.555190E+04, 1.563614E+04, 1.572061E+04,
1.580534E+04, 1.589030E+04, 1.597551E+04, 1.606097E+04, 1.614667E+04, 1.623262E+04,
1.631881E+04, 1.640524E+04, 1.649192E+04, 1.657885E+04, 1.666602E+04, 1.675344E+04,
1.684110E+04, 1.692901E+04, 1.701716E+04, 1.710556E+04, 1.719420E+04, 1.728309E+04,
1.737222E+04, 1.746161E+04, 1.755123E+04, 1.764111E+04, 1.773123E+04, 1.782159E+04,
1.791221E+04, 1.800306E+04, 1.809417E+04, 1.818552E+04, 1.827712E+04, 1.836897E+04,
1.846106E+04, 1.855340E+04, 1.864598E+04, 1.873882E+04, 1.883190E+04, 1.892522E+04,
1.901880E+04, 1.911262E+04, 1.920669E+04, 1.930100E+04, 1.939557E+04, 1.949038E+04,
1.958544E+04, 1.968074E+04, 1.977629E+04, 1.987210E+04, 1.996815E+04, 2.006444E+04,
2.016099E+04, 2.025778E+04, 2.035482E+04, 2.045211E+04, 2.054965E+04, 2.064743E+04,
2.074547E+04, 2.084375E+04, 2.094228E+04, 2.104106E+04, 2.114008E+04, 2.123936E+04,
2.133888E+04, 2.143865E+04, 2.153867E+04, 2.163894E+04, 2.173946E+04, 2.184022E+04,
2.194124E+04, 2.204250E+04, 2.214402E+04, 2.224578E+04, 2.234779E+04, 2.245005E+04,
2.255255E+04, 2.265531E+04, 2.275832E+04, 2.286157E+04, 2.296507E+04, 2.306883E+04,
2.317283E+04, 2.327708E+04, 2.338158E+04, 2.348633E+04, 2.359133E+04, 2.369657E+04,
2.380207E+04, 2.390782E+04, 2.401381E+04, 2.412006E+04, 2.422655E+04, 2.433329E+04,
2.444028E+04, 2.454752E+04, 2.465502E+04, 2.476276E+04, 2.487074E+04, 2.497898E+04,
2.508747E+04, 2.519621E+04, 2.530519E+04, 2.541443E+04, 2.552392E+04, 2.563365E+04,
2.574363E+04,
])
# ---------------------- M = 5, I = 3 ---------------------------
M = 5
I = 3
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[3]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.015450E+00, 7.934590E+00, 1.552296E+01, 2.311511E+01, 3.070871E+01, 3.830330E+01,
4.589875E+01, 5.349498E+01, 6.109197E+01, 6.868969E+01, 7.628815E+01, 8.388739E+01,
9.148750E+01, 9.908871E+01, 1.066914E+02, 1.142961E+02, 1.219039E+02, 1.295159E+02,
1.371337E+02, 1.447592E+02, 1.523947E+02, 1.600426E+02, 1.677058E+02, 1.753873E+02,
1.830902E+02, 1.908178E+02, 1.985735E+02, 2.063605E+02, 2.141824E+02, 2.220424E+02,
2.299438E+02, 2.378898E+02, 2.458836E+02, 2.539281E+02, 2.620262E+02, 2.701807E+02,
2.783942E+02, 2.866693E+02, 2.950083E+02, 3.034135E+02, 3.118870E+02, 3.204310E+02,
3.290472E+02, 3.377375E+02, 3.465036E+02, 3.553472E+02, 3.642697E+02, 3.732725E+02,
3.823570E+02, 3.915246E+02, 4.007762E+02, 4.101132E+02, 4.195365E+02, 4.290471E+02,
4.386461E+02, 4.483341E+02, 4.581122E+02, 4.679810E+02, 4.779414E+02, 4.879940E+02,
4.981395E+02, 5.083786E+02, 5.187117E+02, 5.291395E+02, 5.396626E+02, 5.502813E+02,
5.609963E+02, 5.718079E+02, 5.827166E+02, 5.937229E+02, 6.048270E+02, 6.160294E+02,
6.273304E+02, 6.387304E+02, 6.502297E+02, 6.618287E+02, 6.735275E+02, 6.853265E+02,
6.972260E+02, 7.092263E+02, 7.213275E+02, 7.335299E+02, 7.458338E+02, 7.582394E+02,
7.707469E+02, 7.833566E+02, 7.960685E+02, 8.088830E+02, 8.218001E+02, 8.348201E+02,
8.479432E+02, 8.611695E+02, 8.744993E+02, 8.879325E+02, 9.014695E+02, 9.151104E+02,
9.288553E+02, 9.427043E+02, 9.566576E+02, 9.707154E+02, 9.848778E+02, 9.991449E+02,
1.013517E+03, 1.027994E+03, 1.042576E+03, 1.057263E+03, 1.072055E+03, 1.086953E+03,
1.101957E+03, 1.117066E+03, 1.132281E+03, 1.147601E+03, 1.163028E+03, 1.178561E+03,
1.194200E+03, 1.209945E+03, 1.225797E+03, 1.241756E+03, 1.257820E+03, 1.273992E+03,
1.290270E+03, 1.306656E+03, 1.323148E+03, 1.339747E+03, 1.356454E+03, 1.373267E+03,
1.390188E+03, 1.407217E+03, 1.424353E+03, 1.441596E+03, 1.458948E+03, 1.476407E+03,
1.493974E+03, 1.511649E+03, 1.529431E+03, 1.547322E+03, 1.565322E+03, 1.583429E+03,
1.601645E+03, 1.619969E+03, 1.638402E+03, 1.656944E+03, 1.675594E+03, 1.694353E+03,
1.713221E+03, 1.732197E+03, 1.751283E+03, 1.770478E+03, 1.789782E+03, 1.809195E+03,
1.828718E+03, 1.848350E+03, 1.868091E+03, 1.887942E+03, 1.907903E+03, 1.927973E+03,
1.948153E+03, 1.968443E+03, 1.988843E+03, 2.009353E+03, 2.029973E+03, 2.050703E+03,
2.071543E+03, 2.092494E+03, 2.113555E+03, 2.134727E+03, 2.156009E+03, 2.177402E+03,
2.198905E+03, 2.220519E+03, 2.242244E+03, 2.264080E+03, 2.286027E+03, 2.308085E+03,
2.330255E+03, 2.352535E+03, 2.374927E+03, 2.397430E+03, 2.420044E+03, 2.442771E+03,
2.465608E+03, 2.488557E+03, 2.511619E+03, 2.534791E+03, 2.558076E+03, 2.581473E+03,
2.604982E+03, 2.628603E+03, 2.652336E+03, 2.676181E+03, 2.700139E+03, 2.724209E+03,
2.748391E+03, 2.772686E+03, 2.797094E+03, 2.821615E+03, 2.846248E+03, 2.870994E+03,
2.895853E+03, 2.920825E+03, 2.945910E+03, 2.971108E+03, 2.996420E+03, 3.021844E+03,
3.047382E+03, 3.073034E+03, 3.098799E+03, 3.124678E+03, 3.150670E+03, 3.176776E+03,
3.202996E+03, 3.229330E+03, 3.255778E+03, 3.282340E+03, 3.309015E+03, 3.335806E+03,
3.362710E+03, 3.389729E+03, 3.416862E+03, 3.444109E+03, 3.471471E+03, 3.498948E+03,
3.526540E+03, 3.554246E+03, 3.582067E+03, 3.610003E+03, 3.638054E+03, 3.666220E+03,
3.694501E+03, 3.722898E+03, 3.751409E+03, 3.780036E+03, 3.808779E+03, 3.837637E+03,
3.866611E+03, 3.895700E+03, 3.924905E+03, 3.954226E+03, 3.983662E+03, 4.013215E+03,
4.042883E+03, 4.072668E+03, 4.102569E+03, 4.132586E+03, 4.162720E+03, 4.192969E+03,
4.223336E+03, 4.253818E+03, 4.284418E+03, 4.315134E+03, 4.345967E+03, 4.376916E+03,
4.407983E+03, 4.439166E+03, 4.470467E+03, 4.501884E+03, 4.533419E+03, 4.565071E+03,
4.596841E+03, 4.628727E+03, 4.660731E+03, 4.692853E+03, 4.725092E+03, 4.757449E+03,
4.789924E+03, 4.822517E+03, 4.855227E+03, 4.888055E+03, 4.921002E+03, 4.954066E+03,
4.987249E+03, 5.020550E+03, 5.053969E+03, 5.087506E+03, 5.121162E+03, 5.154936E+03,
5.188829E+03, 5.222841E+03, 5.256971E+03, 5.291221E+03, 5.325588E+03, 5.360075E+03,
5.394681E+03, 5.429406E+03, 5.464250E+03, 5.499213E+03, 5.534296E+03, 5.569497E+03,
5.604818E+03, 5.640259E+03, 5.675819E+03, 5.711498E+03, 5.747298E+03, 5.783217E+03,
5.819255E+03, 5.855414E+03, 5.891692E+03, 5.928090E+03, 5.964609E+03, 6.001247E+03,
6.038006E+03, 6.074885E+03, 6.111884E+03, 6.149003E+03, 6.186243E+03, 6.223603E+03,
6.261084E+03, 6.298685E+03, 6.336407E+03, 6.374250E+03, 6.412213E+03, 6.450298E+03,
6.488503E+03, 6.526829E+03, 6.565276E+03, 6.603844E+03, 6.642533E+03, 6.681344E+03,
6.720275E+03, 6.759328E+03, 6.798502E+03, 6.837798E+03, 6.877215E+03, 6.916754E+03,
6.956414E+03, 6.996196E+03, 7.036099E+03, 7.076125E+03, 7.116272E+03, 7.156541E+03,
7.196931E+03, 7.237444E+03, 7.278079E+03, 7.318835E+03, 7.359714E+03, 7.400715E+03,
7.441839E+03, 7.483084E+03, 7.524452E+03, 7.565942E+03, 7.607555E+03, 7.649290E+03,
7.691147E+03, 7.733127E+03, 7.775230E+03, 7.817455E+03, 7.859804E+03, 7.902274E+03,
7.944868E+03, 7.987585E+03, 8.030424E+03, 8.073386E+03, 8.116471E+03, 8.159680E+03,
8.203011E+03, 8.246466E+03, 8.290043E+03, 8.333744E+03, 8.377568E+03, 8.421516E+03,
8.465586E+03, 8.509781E+03, 8.554098E+03, 8.598539E+03, 8.643104E+03, 8.687792E+03,
8.732603E+03, 8.777538E+03, 8.822597E+03, 8.867780E+03, 8.913086E+03, 8.958516E+03,
9.004070E+03, 9.049748E+03, 9.095549E+03, 9.141475E+03, 9.187524E+03, 9.233697E+03,
9.279995E+03, 9.326416E+03, 9.372962E+03, 9.419631E+03, 9.466425E+03, 9.513343E+03,
9.560385E+03, 9.607552E+03, 9.654843E+03, 9.702258E+03, 9.749797E+03, 9.797461E+03,
9.845249E+03, 9.893161E+03, 9.941198E+03, 9.989360E+03, 1.003765E+04, 1.008606E+04,
1.013459E+04, 1.018325E+04, 1.023204E+04, 1.028094E+04, 1.032998E+04, 1.037914E+04,
1.042842E+04, 1.047783E+04, 1.052736E+04, 1.057702E+04, 1.062680E+04, 1.067670E+04,
1.072674E+04, 1.077689E+04, 1.082717E+04, 1.087758E+04, 1.092811E+04, 1.097877E+04,
1.102955E+04, 1.108045E+04, 1.113149E+04, 1.118264E+04, 1.123392E+04, 1.128533E+04,
1.133686E+04, 1.138851E+04, 1.144030E+04, 1.149220E+04, 1.154423E+04, 1.159639E+04,
1.164867E+04, 1.170108E+04, 1.175361E+04, 1.180627E+04, 1.185905E+04, 1.191196E+04,
1.196499E+04, 1.201815E+04, 1.207143E+04, 1.212484E+04, 1.217837E+04, 1.223203E+04,
1.228581E+04, 1.233972E+04, 1.239375E+04, 1.244791E+04, 1.250220E+04, 1.255661E+04,
1.261114E+04, 1.266580E+04, 1.272059E+04, 1.277550E+04, 1.283053E+04, 1.288570E+04,
1.294098E+04,
])
# ---------------------- M = 5, I = 4 ---------------------------
M = 5
I = 4
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[3]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
6.081920E+00, 4.656398E+01, 9.104865E+01, 1.355563E+02, 1.800726E+02, 2.245949E+02,
2.691223E+02, 3.136542E+02, 3.581906E+02, 4.027313E+02, 4.472763E+02, 4.918259E+02,
5.363805E+02, 5.809412E+02, 6.255100E+02, 6.700904E+02, 7.146872E+02, 7.593070E+02,
8.039587E+02, 8.486526E+02, 8.934013E+02, 9.382189E+02, 9.831211E+02, 1.028125E+03,
1.073248E+03, 1.118509E+03, 1.163927E+03, 1.209521E+03, 1.255311E+03, 1.301316E+03,
1.347555E+03, 1.394046E+03, 1.440807E+03, 1.487857E+03, 1.535211E+03, 1.582885E+03,
1.630896E+03, 1.679258E+03, 1.727985E+03, 1.777089E+03, 1.826585E+03, 1.876483E+03,
1.926795E+03, 1.977532E+03, 2.028704E+03, 2.080320E+03, 2.132389E+03, 2.184919E+03,
2.237919E+03, 2.291396E+03, 2.345357E+03, 2.399809E+03, 2.454758E+03, 2.510210E+03,
2.566171E+03, 2.622645E+03, 2.679638E+03, 2.737155E+03, 2.795199E+03, 2.853775E+03,
2.912888E+03, 2.972540E+03, 3.032735E+03, 3.093477E+03, 3.154769E+03, 3.216614E+03,
3.279014E+03, 3.341973E+03, 3.405493E+03, 3.469576E+03, 3.534226E+03, 3.599443E+03,
3.665230E+03, 3.731590E+03, 3.798524E+03, 3.866034E+03, 3.934122E+03, 4.002789E+03,
4.072038E+03, 4.141869E+03, 4.212285E+03, 4.283286E+03, 4.354874E+03, 4.427051E+03,
4.499818E+03, 4.573175E+03, 4.647125E+03, 4.721668E+03, 4.796805E+03, 4.872538E+03,
4.948867E+03, 5.025794E+03, 5.103320E+03, 5.181445E+03, 5.260171E+03, 5.339498E+03,
5.419427E+03, 5.499959E+03, 5.581096E+03, 5.662837E+03, 5.745183E+03, 5.828137E+03,
5.911697E+03, 5.995865E+03, 6.080641E+03, 6.166027E+03, 6.252023E+03, 6.338630E+03,
6.425848E+03, 6.513677E+03, 6.602120E+03, 6.691176E+03, 6.780846E+03, 6.871130E+03,
6.962030E+03, 7.053545E+03, 7.145676E+03, 7.238425E+03, 7.331791E+03, 7.425775E+03,
7.520378E+03, 7.615600E+03, 7.711441E+03, 7.807903E+03, 7.904986E+03, 8.002690E+03,
8.101016E+03, 8.199964E+03, 8.299536E+03, 8.399730E+03, 8.500549E+03, 8.601992E+03,
8.704060E+03, 8.806753E+03, 8.910073E+03, 9.014018E+03, 9.118591E+03, 9.223791E+03,
9.329619E+03, 9.436075E+03, 9.543160E+03, 9.650874E+03, 9.759219E+03, 9.868193E+03,
9.977798E+03, 1.008803E+04, 1.019890E+04, 1.031040E+04, 1.042253E+04, 1.053530E+04,
1.064870E+04, 1.076273E+04, 1.087740E+04, 1.099270E+04, 1.110864E+04, 1.122521E+04,
1.134242E+04, 1.146026E+04, 1.157875E+04, 1.169787E+04, 1.181763E+04, 1.193802E+04,
1.205906E+04, 1.218073E+04, 1.230305E+04, 1.242600E+04, 1.254960E+04, 1.267383E+04,
1.279871E+04, 1.292423E+04, 1.305039E+04, 1.317719E+04, 1.330464E+04, 1.343273E+04,
1.356147E+04, 1.369085E+04, 1.382087E+04, 1.395154E+04, 1.408286E+04, 1.421482E+04,
1.434743E+04, 1.448068E+04, 1.461459E+04, 1.474914E+04, 1.488434E+04, 1.502019E+04,
1.515669E+04, 1.529383E+04, 1.543163E+04, 1.557008E+04, 1.570918E+04, 1.584893E+04,
1.598933E+04, 1.613039E+04, 1.627209E+04, 1.641445E+04, 1.655747E+04, 1.670114E+04,
1.684546E+04, 1.699044E+04, 1.713607E+04, 1.728236E+04, 1.742930E+04, 1.757690E+04,
1.772516E+04, 1.787408E+04, 1.802365E+04, 1.817388E+04, 1.832477E+04, 1.847632E+04,
1.862853E+04, 1.878140E+04, 1.893493E+04, 1.908912E+04, 1.924398E+04, 1.939949E+04,
1.955567E+04, 1.971250E+04, 1.987001E+04, 2.002817E+04, 2.018700E+04, 2.034649E+04,
2.050665E+04, 2.066748E+04, 2.082897E+04, 2.099112E+04, 2.115394E+04, 2.131743E+04,
2.148159E+04, 2.164641E+04, 2.181190E+04, 2.197807E+04, 2.214490E+04, 2.231239E+04,
2.248056E+04, 2.264940E+04, 2.281891E+04, 2.298910E+04, 2.315995E+04, 2.333147E+04,
2.350367E+04, 2.367654E+04, 2.385009E+04, 2.402431E+04, 2.419920E+04, 2.437476E+04,
2.455101E+04, 2.472792E+04, 2.490552E+04, 2.508379E+04, 2.526273E+04, 2.544236E+04,
2.562266E+04, 2.580364E+04, 2.598529E+04, 2.616763E+04, 2.635065E+04, 2.653434E+04,
2.671872E+04, 2.690377E+04, 2.708951E+04, 2.727593E+04, 2.746303E+04, 2.765081E+04,
2.783928E+04, 2.802843E+04, 2.821826E+04, 2.840877E+04, 2.859997E+04, 2.879186E+04,
2.898443E+04, 2.917768E+04, 2.937162E+04, 2.956625E+04, 2.976157E+04, 2.995757E+04,
3.015426E+04, 3.035163E+04, 3.054970E+04, 3.074845E+04, 3.094790E+04, 3.114803E+04,
3.134885E+04, 3.155037E+04, 3.175257E+04, 3.195547E+04, 3.215905E+04, 3.236333E+04,
3.256830E+04, 3.277397E+04, 3.298033E+04, 3.318738E+04, 3.339512E+04, 3.360356E+04,
3.381270E+04, 3.402252E+04, 3.423305E+04, 3.444427E+04, 3.465619E+04, 3.486880E+04,
3.508211E+04, 3.529612E+04, 3.551082E+04, 3.572623E+04, 3.594233E+04, 3.615913E+04,
3.637663E+04, 3.659483E+04, 3.681373E+04, 3.703333E+04, 3.725363E+04, 3.747464E+04,
3.769634E+04, 3.791875E+04, 3.814186E+04, 3.836567E+04, 3.859018E+04, 3.881540E+04,
3.904132E+04, 3.926794E+04, 3.949527E+04, 3.972330E+04, 3.995204E+04, 4.018149E+04,
4.041164E+04, 4.064249E+04, 4.087405E+04, 4.110632E+04, 4.133930E+04, 4.157298E+04,
4.180737E+04, 4.204247E+04, 4.227828E+04, 4.251480E+04, 4.275202E+04, 4.298996E+04,
4.322860E+04, 4.346796E+04, 4.370802E+04, 4.394880E+04, 4.419028E+04, 4.443248E+04,
4.467539E+04, 4.491901E+04, 4.516335E+04, 4.540839E+04, 4.565415E+04, 4.590062E+04,
4.614781E+04, 4.639571E+04, 4.664432E+04, 4.689365E+04, 4.714369E+04, 4.739445E+04,
4.764592E+04, 4.789811E+04, 4.815101E+04, 4.840463E+04, 4.865897E+04, 4.891402E+04,
4.916979E+04, 4.942627E+04, 4.968348E+04, 4.994140E+04, 5.020004E+04, 5.045939E+04,
5.071947E+04, 5.098026E+04, 5.124178E+04, 5.150401E+04, 5.176696E+04, 5.203063E+04,
5.229502E+04, 5.256014E+04, 5.282597E+04, 5.309252E+04, 5.335979E+04, 5.362779E+04,
5.389650E+04, 5.416594E+04, 5.443610E+04, 5.470698E+04, 5.497858E+04, 5.525091E+04,
5.552396E+04, 5.579773E+04, 5.607222E+04, 5.634744E+04, 5.662338E+04, 5.690004E+04,
5.717743E+04, 5.745555E+04, 5.773438E+04, 5.801394E+04, 5.829423E+04, 5.857524E+04,
5.885698E+04, 5.913944E+04, 5.942262E+04, 5.970654E+04, 5.999117E+04, 6.027654E+04,
6.056263E+04, 6.084944E+04, 6.113698E+04, 6.142525E+04, 6.171425E+04, 6.200397E+04,
6.229442E+04, 6.258560E+04, 6.287750E+04, 6.317013E+04, 6.346349E+04, 6.375758E+04,
6.405239E+04, 6.434794E+04, 6.464421E+04, 6.494121E+04, 6.523894E+04, 6.553739E+04,
6.583658E+04, 6.613649E+04, 6.643713E+04, 6.673850E+04, 6.704060E+04, 6.734343E+04,
6.764699E+04, 6.795128E+04, 6.825630E+04, 6.856205E+04, 6.886853E+04, 6.917573E+04,
6.948367E+04, 6.979234E+04, 7.010173E+04, 7.041186E+04, 7.072272E+04, 7.103431E+04,
7.134663E+04, 7.165967E+04, 7.197345E+04, 7.228796E+04, 7.260320E+04, 7.291917E+04,
7.323588E+04, 7.355331E+04, 7.387147E+04, 7.419036E+04, 7.450999E+04, 7.483035E+04,
7.515143E+04,
])
# ---------------------- M = 5, I = 5 ---------------------------
M = 5
I = 5
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[3]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
2.039420E+00, 1.660365E+01, 3.251624E+01, 4.843614E+01, 6.435894E+01, 8.028378E+01,
9.621037E+01, 1.121386E+02, 1.280684E+02, 1.439997E+02, 1.599326E+02, 1.758672E+02,
1.918036E+02, 2.077426E+02, 2.236851E+02, 2.396326E+02, 2.555873E+02, 2.715523E+02,
2.875312E+02, 3.035287E+02, 3.195498E+02, 3.356005E+02, 3.516871E+02, 3.678163E+02,
3.839953E+02, 4.002313E+02, 4.165317E+02, 4.329040E+02, 4.493555E+02, 4.658935E+02,
4.825251E+02, 4.992572E+02, 5.160964E+02, 5.330491E+02, 5.501215E+02, 5.673195E+02,
5.846486E+02, 6.021141E+02, 6.197210E+02, 6.374741E+02, 6.553778E+02, 6.734364E+02,
6.916539E+02, 7.100339E+02, 7.285802E+02, 7.472958E+02, 7.661840E+02, 7.852478E+02,
8.044898E+02, 8.239127E+02, 8.435189E+02, 8.633107E+02, 8.832903E+02, 9.034597E+02,
9.238208E+02, 9.443754E+02, 9.651252E+02, 9.860718E+02, 1.007217E+03, 1.028562E+03,
1.050107E+03, 1.071856E+03, 1.093807E+03, 1.115964E+03, 1.138326E+03, 1.160895E+03,
1.183672E+03, 1.206658E+03, 1.229853E+03, 1.253259E+03, 1.276876E+03, 1.300705E+03,
1.324747E+03, 1.349002E+03, 1.373471E+03, 1.398155E+03, 1.423054E+03, 1.448169E+03,
1.473501E+03, 1.499050E+03, 1.524816E+03, 1.550800E+03, 1.577002E+03, 1.603424E+03,
1.630065E+03, 1.656925E+03, 1.684006E+03, 1.711308E+03, 1.738830E+03, 1.766574E+03,
1.794539E+03, 1.822727E+03, 1.851137E+03, 1.879770E+03, 1.908625E+03, 1.937705E+03,
1.967007E+03, 1.996534E+03, 2.026285E+03, 2.056261E+03, 2.086461E+03, 2.116887E+03,
2.147537E+03, 2.178414E+03, 2.209516E+03, 2.240845E+03, 2.272400E+03, 2.304181E+03,
2.336189E+03, 2.368425E+03, 2.400887E+03, 2.433577E+03, 2.466495E+03, 2.499641E+03,
2.533015E+03, 2.566618E+03, 2.600449E+03, 2.634509E+03, 2.668798E+03, 2.703316E+03,
2.738063E+03, 2.773040E+03, 2.808247E+03, 2.843684E+03, 2.879351E+03, 2.915249E+03,
2.951377E+03, 2.987736E+03, 3.024325E+03, 3.061146E+03, 3.098198E+03, 3.135482E+03,
3.172998E+03, 3.210745E+03, 3.248724E+03, 3.286935E+03, 3.325379E+03, 3.364056E+03,
3.402965E+03, 3.442107E+03, 3.481482E+03, 3.521090E+03, 3.560932E+03, 3.601007E+03,
3.641316E+03, 3.681859E+03, 3.722636E+03, 3.763647E+03, 3.804893E+03, 3.846373E+03,
3.888088E+03, 3.930038E+03, 3.972223E+03, 4.014644E+03, 4.057299E+03, 4.100190E+03,
4.143317E+03, 4.186680E+03, 4.230279E+03, 4.274114E+03, 4.318186E+03, 4.362494E+03,
4.407039E+03, 4.451821E+03, 4.496839E+03, 4.542095E+03, 4.587588E+03, 4.633319E+03,
4.679287E+03, 4.725494E+03, 4.771938E+03, 4.818620E+03, 4.865541E+03, 4.912700E+03,
4.960098E+03, 5.007734E+03, 5.055610E+03, 5.103724E+03, 5.152078E+03, 5.200672E+03,
5.249504E+03, 5.298577E+03, 5.347889E+03, 5.397442E+03, 5.447235E+03, 5.497268E+03,
5.547541E+03, 5.598056E+03, 5.648811E+03, 5.699807E+03, 5.751044E+03, 5.802523E+03,
5.854243E+03, 5.906205E+03, 5.958408E+03, 6.010853E+03, 6.063541E+03, 6.116471E+03,
6.169643E+03, 6.223058E+03, 6.276715E+03, 6.330616E+03, 6.384759E+03, 6.439146E+03,
6.493776E+03, 6.548649E+03, 6.603766E+03, 6.659127E+03, 6.714732E+03, 6.770581E+03,
6.826675E+03, 6.883013E+03, 6.939595E+03, 6.996422E+03, 7.053495E+03, 7.110812E+03,
7.168374E+03, 7.226182E+03, 7.284235E+03, 7.342534E+03, 7.401079E+03, 7.459870E+03,
7.518907E+03, 7.578191E+03, 7.637721E+03, 7.697497E+03, 7.757520E+03, 7.817791E+03,
7.878308E+03, 7.939072E+03, 8.000084E+03, 8.061344E+03, 8.122851E+03, 8.184606E+03,
8.246609E+03, 8.308860E+03, 8.371360E+03, 8.434108E+03, 8.497104E+03, 8.560349E+03,
8.623844E+03, 8.687587E+03, 8.751580E+03, 8.815821E+03, 8.880313E+03, 8.945054E+03,
9.010045E+03, 9.075286E+03, 9.140777E+03, 9.206518E+03, 9.272509E+03, 9.338752E+03,
9.405244E+03, 9.471988E+03, 9.538983E+03, 9.606229E+03, 9.673726E+03, 9.741475E+03,
9.809475E+03, 9.877727E+03, 9.946231E+03, 1.001499E+04, 1.008399E+04, 1.015325E+04,
1.022277E+04, 1.029253E+04, 1.036255E+04, 1.043282E+04, 1.050335E+04, 1.057412E+04,
1.064515E+04, 1.071644E+04, 1.078797E+04, 1.085976E+04, 1.093181E+04, 1.100411E+04,
1.107666E+04, 1.114947E+04, 1.122253E+04, 1.129585E+04, 1.136942E+04, 1.144324E+04,
1.151732E+04, 1.159166E+04, 1.166625E+04, 1.174110E+04, 1.181620E+04, 1.189155E+04,
1.196717E+04, 1.204304E+04, 1.211916E+04, 1.219554E+04, 1.227218E+04, 1.234907E+04,
1.242622E+04, 1.250363E+04, 1.258129E+04, 1.265921E+04, 1.273739E+04, 1.281582E+04,
1.289451E+04, 1.297346E+04, 1.305267E+04, 1.313213E+04, 1.321185E+04, 1.329183E+04,
1.337207E+04, 1.345256E+04, 1.353332E+04, 1.361433E+04, 1.369560E+04, 1.377713E+04,
1.385891E+04, 1.394096E+04, 1.402326E+04, 1.410583E+04, 1.418865E+04, 1.427173E+04,
1.435507E+04, 1.443867E+04, 1.452253E+04, 1.460665E+04, 1.469103E+04, 1.477567E+04,
1.486057E+04, 1.494572E+04, 1.503114E+04, 1.511682E+04, 1.520276E+04, 1.528896E+04,
1.537542E+04, 1.546214E+04, 1.554912E+04, 1.563636E+04, 1.572387E+04, 1.581163E+04,
1.589966E+04, 1.598794E+04, 1.607649E+04, 1.616530E+04, 1.625437E+04, 1.634370E+04,
1.643329E+04, 1.652315E+04, 1.661326E+04, 1.670364E+04, 1.679428E+04, 1.688518E+04,
1.697635E+04, 1.706777E+04, 1.715946E+04, 1.725141E+04, 1.734363E+04, 1.743610E+04,
1.752884E+04, 1.762184E+04, 1.771510E+04, 1.780863E+04, 1.790242E+04, 1.799647E+04,
1.809078E+04, 1.818536E+04, 1.828020E+04, 1.837531E+04, 1.847067E+04, 1.856630E+04,
1.866220E+04, 1.875836E+04, 1.885478E+04, 1.895146E+04, 1.904841E+04, 1.914562E+04,
1.924309E+04, 1.934083E+04, 1.943883E+04, 1.953710E+04, 1.963563E+04, 1.973443E+04,
1.983348E+04, 1.993281E+04, 2.003239E+04, 2.013224E+04, 2.023236E+04, 2.033273E+04,
2.043338E+04, 2.053428E+04, 2.063546E+04, 2.073689E+04, 2.083859E+04, 2.094056E+04,
2.104279E+04, 2.114528E+04, 2.124804E+04, 2.135106E+04, 2.145435E+04, 2.155790E+04,
2.166172E+04, 2.176580E+04, 2.187014E+04, 2.197476E+04, 2.207963E+04, 2.218477E+04,
2.229018E+04, 2.239585E+04, 2.250178E+04, 2.260798E+04, 2.271445E+04, 2.282118E+04,
2.292817E+04, 2.303543E+04, 2.314296E+04, 2.325075E+04, 2.335880E+04, 2.346712E+04,
2.357571E+04, 2.368455E+04, 2.379367E+04, 2.390305E+04, 2.401269E+04, 2.412260E+04,
2.423278E+04, 2.434322E+04, 2.445392E+04, 2.456489E+04, 2.467613E+04, 2.478763E+04,
2.489939E+04, 2.501142E+04, 2.512372E+04, 2.523628E+04, 2.534910E+04, 2.546220E+04,
2.557555E+04, 2.568917E+04, 2.580306E+04, 2.591721E+04, 2.603162E+04, 2.614630E+04,
2.626125E+04, 2.637646E+04, 2.649193E+04, 2.660767E+04, 2.672367E+04, 2.683994E+04,
2.695648E+04, 2.707328E+04, 2.719034E+04, 2.730767E+04, 2.742526E+04, 2.754312E+04,
2.766124E+04,
])
# ---------------------- M = 5, I = 6 ---------------------------
M = 5
I = 6
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[3]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.220898E+01, 9.732970E+01, 1.905089E+02, 2.837327E+02, 3.769738E+02, 4.702271E+02,
5.634907E+02, 6.567639E+02, 7.500463E+02, 8.433378E+02, 9.366383E+02, 1.029949E+03,
1.123270E+03, 1.216605E+03, 1.309959E+03, 1.403341E+03, 1.496762E+03, 1.590239E+03,
1.683792E+03, 1.777446E+03, 1.871230E+03, 1.965177E+03, 2.059322E+03, 2.153704E+03,
2.248362E+03, 2.343338E+03, 2.438674E+03, 2.534412E+03, 2.630596E+03, 2.727266E+03,
2.824463E+03, 2.922228E+03, 3.020599E+03, 3.119613E+03, 3.219306E+03, 3.319712E+03,
3.420863E+03, 3.522791E+03, 3.625525E+03, 3.729092E+03, 3.833519E+03, 3.938831E+03,
4.045052E+03, 4.152203E+03, 4.260305E+03, 4.369377E+03, 4.479439E+03, 4.590508E+03,
4.702599E+03, 4.815729E+03, 4.929912E+03, 5.045161E+03, 5.161489E+03, 5.278909E+03,
5.397431E+03, 5.517067E+03, 5.637826E+03, 5.759718E+03, 5.882752E+03, 6.006937E+03,
6.132280E+03, 6.258790E+03, 6.386472E+03, 6.515335E+03, 6.645385E+03, 6.776627E+03,
6.909068E+03, 7.042712E+03, 7.177566E+03, 7.313634E+03, 7.450922E+03, 7.589432E+03,
7.729171E+03, 7.870141E+03, 8.012348E+03, 8.155794E+03, 8.300483E+03, 8.446420E+03,
8.593606E+03, 8.742045E+03, 8.891741E+03, 9.042696E+03, 9.194913E+03, 9.348394E+03,
9.503144E+03, 9.659162E+03, 9.816454E+03, 9.975020E+03, 1.013486E+04, 1.029599E+04,
1.045839E+04, 1.062208E+04, 1.078705E+04, 1.095331E+04, 1.112086E+04, 1.128970E+04,
1.145983E+04, 1.163126E+04, 1.180398E+04, 1.197801E+04, 1.215333E+04, 1.232995E+04,
1.250788E+04, 1.268711E+04, 1.286765E+04, 1.304949E+04, 1.323264E+04, 1.341711E+04,
1.360288E+04, 1.378997E+04, 1.397837E+04, 1.416809E+04, 1.435913E+04, 1.455148E+04,
1.474515E+04, 1.494015E+04, 1.513646E+04, 1.533410E+04, 1.553307E+04, 1.573336E+04,
1.593497E+04, 1.613792E+04, 1.634219E+04, 1.654779E+04, 1.675473E+04, 1.696300E+04,
1.717260E+04, 1.738353E+04, 1.759580E+04, 1.780941E+04, 1.802436E+04, 1.824064E+04,
1.845827E+04, 1.867723E+04, 1.889754E+04, 1.911919E+04, 1.934219E+04, 1.956653E+04,
1.979221E+04, 2.001925E+04, 2.024763E+04, 2.047736E+04, 2.070844E+04, 2.094087E+04,
2.117466E+04, 2.140979E+04, 2.164629E+04, 2.188413E+04, 2.212333E+04, 2.236389E+04,
2.260581E+04, 2.284909E+04, 2.309373E+04, 2.333972E+04, 2.358708E+04, 2.383580E+04,
2.408589E+04, 2.433734E+04, 2.459015E+04, 2.484434E+04, 2.509989E+04, 2.535680E+04,
2.561509E+04, 2.587475E+04, 2.613578E+04, 2.639818E+04, 2.666195E+04, 2.692710E+04,
2.719362E+04, 2.746152E+04, 2.773080E+04, 2.800145E+04, 2.827348E+04, 2.854690E+04,
2.882169E+04, 2.909786E+04, 2.937542E+04, 2.965436E+04, 2.993468E+04, 3.021639E+04,
3.049948E+04, 3.078396E+04, 3.106983E+04, 3.135709E+04, 3.164574E+04, 3.193578E+04,
3.222721E+04, 3.252003E+04, 3.281424E+04, 3.310985E+04, 3.340686E+04, 3.370526E+04,
3.400506E+04, 3.430625E+04, 3.460885E+04, 3.491284E+04, 3.521824E+04, 3.552504E+04,
3.583324E+04, 3.614284E+04, 3.645385E+04, 3.676626E+04, 3.708008E+04, 3.739530E+04,
3.771194E+04, 3.802998E+04, 3.834943E+04, 3.867030E+04, 3.899257E+04, 3.931626E+04,
3.964136E+04, 3.996788E+04, 4.029581E+04, 4.062516E+04, 4.095592E+04, 4.128810E+04,
4.162170E+04, 4.195673E+04, 4.229317E+04, 4.263103E+04, 4.297032E+04, 4.331103E+04,
4.365316E+04, 4.399672E+04, 4.434171E+04, 4.468812E+04, 4.503596E+04, 4.538523E+04,
4.573593E+04, 4.608806E+04, 4.644162E+04, 4.679662E+04, 4.715304E+04, 4.751091E+04,
4.787020E+04, 4.823094E+04, 4.859311E+04, 4.895671E+04, 4.932176E+04, 4.968825E+04,
5.005618E+04, 5.042554E+04, 5.079636E+04, 5.116861E+04, 5.154231E+04, 5.191745E+04,
5.229404E+04, 5.267208E+04, 5.305156E+04, 5.343249E+04, 5.381488E+04, 5.419871E+04,
5.458399E+04, 5.497073E+04, 5.535892E+04, 5.574856E+04, 5.613965E+04, 5.653221E+04,
5.692621E+04, 5.732168E+04, 5.771860E+04, 5.811699E+04, 5.851683E+04, 5.891813E+04,
5.932090E+04, 5.972512E+04, 6.013081E+04, 6.053797E+04, 6.094659E+04, 6.135667E+04,
6.176822E+04, 6.218124E+04, 6.259573E+04, 6.301168E+04, 6.342911E+04, 6.384801E+04,
6.426837E+04, 6.469022E+04, 6.511353E+04, 6.553832E+04, 6.596458E+04, 6.639232E+04,
6.682153E+04, 6.725222E+04, 6.768439E+04, 6.811804E+04, 6.855317E+04, 6.898978E+04,
6.942787E+04, 6.986744E+04, 7.030849E+04, 7.075103E+04, 7.119505E+04, 7.164056E+04,
7.208755E+04, 7.253603E+04, 7.298599E+04, 7.343745E+04, 7.389039E+04, 7.434483E+04,
7.480075E+04, 7.525816E+04, 7.571707E+04, 7.617747E+04, 7.663936E+04, 7.710275E+04,
7.756763E+04, 7.803400E+04, 7.850187E+04, 7.897124E+04, 7.944211E+04, 7.991447E+04,
8.038834E+04, 8.086370E+04, 8.134057E+04, 8.181893E+04, 8.229880E+04, 8.278016E+04,
8.326304E+04, 8.374741E+04, 8.423329E+04, 8.472068E+04, 8.520957E+04, 8.569996E+04,
8.619187E+04, 8.668528E+04, 8.718020E+04, 8.767663E+04, 8.817457E+04, 8.867401E+04,
8.917497E+04, 8.967744E+04, 9.018143E+04, 9.068692E+04, 9.119393E+04, 9.170245E+04,
9.221249E+04, 9.272404E+04, 9.323710E+04, 9.375169E+04, 9.426779E+04, 9.478540E+04,
9.530454E+04, 9.582519E+04, 9.634736E+04, 9.687105E+04, 9.739626E+04, 9.792299E+04,
9.845125E+04, 9.898102E+04, 9.951232E+04, 1.000451E+05, 1.005795E+05, 1.011153E+05,
1.016527E+05, 1.021916E+05, 1.027321E+05, 1.032740E+05, 1.038175E+05, 1.043626E+05,
1.049091E+05, 1.054572E+05, 1.060068E+05, 1.065579E+05, 1.071105E+05, 1.076647E+05,
1.082204E+05, 1.087777E+05, 1.093365E+05, 1.098968E+05, 1.104586E+05, 1.110220E+05,
1.115869E+05, 1.121533E+05, 1.127213E+05, 1.132908E+05, 1.138618E+05, 1.144344E+05,
1.150085E+05, 1.155841E+05, 1.161613E+05, 1.167400E+05, 1.173202E+05, 1.179020E+05,
1.184853E+05, 1.190701E+05, 1.196565E+05, 1.202444E+05, 1.208339E+05, 1.214249E+05,
1.220174E+05, 1.226115E+05, 1.232071E+05, 1.238043E+05, 1.244030E+05, 1.250032E+05,
1.256050E+05, 1.262083E+05, 1.268131E+05, 1.274195E+05, 1.280275E+05, 1.286369E+05,
1.292479E+05, 1.298605E+05, 1.304746E+05, 1.310902E+05, 1.317074E+05, 1.323261E+05,
1.329464E+05, 1.335682E+05, 1.341915E+05, 1.348164E+05, 1.354429E+05, 1.360708E+05,
1.367004E+05, 1.373314E+05, 1.379640E+05, 1.385982E+05, 1.392339E+05, 1.398711E+05,
1.405099E+05, 1.411502E+05, 1.417921E+05, 1.424355E+05, 1.430804E+05, 1.437269E+05,
1.443750E+05, 1.450246E+05, 1.456757E+05, 1.463284E+05, 1.469826E+05, 1.476384E+05,
1.482957E+05, 1.489545E+05, 1.496149E+05, 1.502769E+05, 1.509404E+05, 1.516054E+05,
1.522720E+05, 1.529401E+05, 1.536098E+05, 1.542810E+05, 1.549538E+05, 1.556281E+05,
1.563039E+05, 1.569813E+05, 1.576602E+05, 1.583407E+05, 1.590227E+05, 1.597063E+05,
1.603914E+05,
])
# ---------------------- M = 5, I = 7 ---------------------------
M = 5
I = 7
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[3]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.018640E+00, 8.214730E+00, 1.608377E+01, 2.395650E+01, 3.183067E+01, 3.970586E+01,
4.758192E+01, 5.545878E+01, 6.333642E+01, 7.121483E+01, 7.909401E+01, 8.697401E+01,
9.485494E+01, 1.027371E+02, 1.106209E+02, 1.185071E+02, 1.263968E+02, 1.342914E+02,
1.421927E+02, 1.501028E+02, 1.580244E+02, 1.659601E+02, 1.739131E+02, 1.818867E+02,
1.898844E+02, 1.979096E+02, 2.059660E+02, 2.140572E+02, 2.221869E+02, 2.303586E+02,
2.385758E+02, 2.468418E+02, 2.551601E+02, 2.635336E+02, 2.719655E+02, 2.804586E+02,
2.890157E+02, 2.976394E+02, 3.063322E+02, 3.150964E+02, 3.239342E+02, 3.328478E+02,
3.418392E+02, 3.509100E+02, 3.600623E+02, 3.692975E+02, 3.786172E+02, 3.880229E+02,
3.975159E+02, 4.070976E+02, 4.167692E+02, 4.265318E+02, 4.363864E+02, 4.463342E+02,
4.563759E+02, 4.665126E+02, 4.767451E+02, 4.870742E+02, 4.975006E+02, 5.080251E+02,
5.186483E+02, 5.293708E+02, 5.401933E+02, 5.511163E+02, 5.621404E+02, 5.732661E+02,
5.844938E+02, 5.958240E+02, 6.072572E+02, 6.187938E+02, 6.304342E+02, 6.421787E+02,
6.540277E+02, 6.659816E+02, 6.780407E+02, 6.902053E+02, 7.024757E+02, 7.148522E+02,
7.273350E+02, 7.399245E+02, 7.526209E+02, 7.654244E+02, 7.783353E+02, 7.913538E+02,
8.044801E+02, 8.177145E+02, 8.310571E+02, 8.445081E+02, 8.580677E+02, 8.717362E+02,
8.855137E+02, 8.994003E+02, 9.133963E+02, 9.275017E+02, 9.417169E+02, 9.560418E+02,
9.704767E+02, 9.850217E+02, 9.996770E+02, 1.014443E+03, 1.029319E+03, 1.044306E+03,
1.059403E+03, 1.074612E+03, 1.089932E+03, 1.105363E+03, 1.120905E+03, 1.136558E+03,
1.152323E+03, 1.168200E+03, 1.184189E+03, 1.200289E+03, 1.216502E+03, 1.232826E+03,
1.249263E+03, 1.265812E+03, 1.282473E+03, 1.299247E+03, 1.316134E+03, 1.333133E+03,
1.350245E+03, 1.367470E+03, 1.384808E+03, 1.402259E+03, 1.419823E+03, 1.437501E+03,
1.455292E+03, 1.473196E+03, 1.491214E+03, 1.509346E+03, 1.527592E+03, 1.545951E+03,
1.564424E+03, 1.583011E+03, 1.601713E+03, 1.620528E+03, 1.639458E+03, 1.658502E+03,
1.677661E+03, 1.696934E+03, 1.716321E+03, 1.735824E+03, 1.755441E+03, 1.775173E+03,
1.795020E+03, 1.814982E+03, 1.835060E+03, 1.855252E+03, 1.875560E+03, 1.895983E+03,
1.916521E+03, 1.937175E+03, 1.957945E+03, 1.978830E+03, 1.999832E+03, 2.020949E+03,
2.042182E+03, 2.063530E+03, 2.084995E+03, 2.106577E+03, 2.128274E+03, 2.150088E+03,
2.172018E+03, 2.194065E+03, 2.216228E+03, 2.238508E+03, 2.260905E+03, 2.283418E+03,
2.306048E+03, 2.328796E+03, 2.351660E+03, 2.374642E+03, 2.397740E+03, 2.420956E+03,
2.444290E+03, 2.467740E+03, 2.491309E+03, 2.514994E+03, 2.538798E+03, 2.562719E+03,
2.586758E+03, 2.610915E+03, 2.635190E+03, 2.659584E+03, 2.684095E+03, 2.708724E+03,
2.733472E+03, 2.758338E+03, 2.783322E+03, 2.808426E+03, 2.833647E+03, 2.858988E+03,
2.884447E+03, 2.910025E+03, 2.935722E+03, 2.961537E+03, 2.987472E+03, 3.013526E+03,
3.039700E+03, 3.065992E+03, 3.092404E+03, 3.118936E+03, 3.145587E+03, 3.172357E+03,
3.199248E+03, 3.226258E+03, 3.253388E+03, 3.280638E+03, 3.308007E+03, 3.335497E+03,
3.363107E+03, 3.390838E+03, 3.418688E+03, 3.446659E+03, 3.474751E+03, 3.502963E+03,
3.531296E+03, 3.559749E+03, 3.588323E+03, 3.617018E+03, 3.645834E+03, 3.674771E+03,
3.703829E+03, 3.733008E+03, 3.762308E+03, 3.791730E+03, 3.821273E+03, 3.850937E+03,
3.880723E+03, 3.910631E+03, 3.940660E+03, 3.970811E+03, 4.001084E+03, 4.031479E+03,
4.061996E+03, 4.092635E+03, 4.123396E+03, 4.154279E+03, 4.185284E+03, 4.216412E+03,
4.247663E+03, 4.279035E+03, 4.310531E+03, 4.342149E+03, 4.373890E+03, 4.405753E+03,
4.437740E+03, 4.469850E+03, 4.502082E+03, 4.534438E+03, 4.566917E+03, 4.599519E+03,
4.632244E+03, 4.665093E+03, 4.698065E+03, 4.731161E+03, 4.764381E+03, 4.797724E+03,
4.831191E+03, 4.864782E+03, 4.898496E+03, 4.932335E+03, 4.966298E+03, 5.000385E+03,
5.034596E+03, 5.068931E+03, 5.103391E+03, 5.137975E+03, 5.172683E+03, 5.207516E+03,
5.242474E+03, 5.277557E+03, 5.312764E+03, 5.348096E+03, 5.383553E+03, 5.419135E+03,
5.454842E+03, 5.490674E+03, 5.526631E+03, 5.562713E+03, 5.598921E+03, 5.635254E+03,
5.671712E+03, 5.708296E+03, 5.745006E+03, 5.781841E+03, 5.818802E+03, 5.855889E+03,
5.893102E+03, 5.930440E+03, 5.967904E+03, 6.005495E+03, 6.043211E+03, 6.081054E+03,
6.119023E+03, 6.157118E+03, 6.195340E+03, 6.233688E+03, 6.272162E+03, 6.310763E+03,
6.349490E+03, 6.388345E+03, 6.427325E+03, 6.466433E+03, 6.505668E+03, 6.545029E+03,
6.584517E+03, 6.624133E+03, 6.663875E+03, 6.703745E+03, 6.743741E+03, 6.783865E+03,
6.824117E+03, 6.864495E+03, 6.905001E+03, 6.945635E+03, 6.986396E+03, 7.027284E+03,
7.068301E+03, 7.109445E+03, 7.150716E+03, 7.192116E+03, 7.233643E+03, 7.275298E+03,
7.317081E+03, 7.358993E+03, 7.401032E+03, 7.443199E+03, 7.485495E+03, 7.527918E+03,
7.570470E+03, 7.613151E+03, 7.655959E+03, 7.698896E+03, 7.741962E+03, 7.785156E+03,
7.828478E+03, 7.871929E+03, 7.915509E+03, 7.959218E+03, 8.003055E+03, 8.047021E+03,
8.091116E+03, 8.135339E+03, 8.179692E+03, 8.224173E+03, 8.268784E+03, 8.313523E+03,
8.358392E+03, 8.403390E+03, 8.448517E+03, 8.493773E+03, 8.539158E+03, 8.584673E+03,
8.630317E+03, 8.676090E+03, 8.721993E+03, 8.768025E+03, 8.814186E+03, 8.860478E+03,
8.906898E+03, 8.953448E+03, 9.000128E+03, 9.046938E+03, 9.093877E+03, 9.140946E+03,
9.188144E+03, 9.235473E+03, 9.282931E+03, 9.330519E+03, 9.378237E+03, 9.426085E+03,
9.474063E+03, 9.522171E+03, 9.570408E+03, 9.618776E+03, 9.667274E+03, 9.715902E+03,
9.764660E+03, 9.813548E+03, 9.862566E+03, 9.911715E+03, 9.960994E+03, 1.001040E+04,
1.005994E+04, 1.010961E+04, 1.015941E+04, 1.020934E+04, 1.025940E+04, 1.030959E+04,
1.035991E+04, 1.041037E+04, 1.046095E+04, 1.051166E+04, 1.056250E+04, 1.061348E+04,
1.066458E+04, 1.071581E+04, 1.076718E+04, 1.081867E+04, 1.087030E+04, 1.092206E+04,
1.097395E+04, 1.102596E+04, 1.107811E+04, 1.113039E+04, 1.118280E+04, 1.123534E+04,
1.128801E+04, 1.134081E+04, 1.139375E+04, 1.144681E+04, 1.150000E+04, 1.155333E+04,
1.160678E+04, 1.166037E+04, 1.171409E+04, 1.176793E+04, 1.182191E+04, 1.187602E+04,
1.193026E+04, 1.198463E+04, 1.203913E+04, 1.209377E+04, 1.214853E+04, 1.220342E+04,
1.225845E+04, 1.231360E+04, 1.236889E+04, 1.242431E+04, 1.247985E+04, 1.253553E+04,
1.259134E+04, 1.264728E+04, 1.270335E+04, 1.275955E+04, 1.281589E+04, 1.287235E+04,
1.292894E+04, 1.298567E+04, 1.304252E+04, 1.309951E+04, 1.315662E+04, 1.321387E+04,
1.327125E+04, 1.332876E+04, 1.338640E+04, 1.344417E+04, 1.350207E+04, 1.356010E+04,
1.361826E+04,
])
# ---------------------- M = 5, I = 8 ---------------------------
M = 5
I = 8
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[3]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.024260E+00, 8.644900E+00, 1.694486E+01, 2.524838E+01, 3.355335E+01, 4.185935E+01,
5.016626E+01, 5.847401E+01, 6.678257E+01, 7.509194E+01, 8.340213E+01, 9.171321E+01,
1.000254E+02, 1.083389E+02, 1.166545E+02, 1.249731E+02, 1.332960E+02, 1.416249E+02,
1.499621E+02, 1.583101E+02, 1.666720E+02, 1.750509E+02, 1.834505E+02, 1.918746E+02,
2.003270E+02, 2.088118E+02, 2.173329E+02, 2.258944E+02, 2.345004E+02, 2.431546E+02,
2.518609E+02, 2.606229E+02, 2.694442E+02, 2.783282E+02, 2.872781E+02, 2.962969E+02,
3.053877E+02, 3.145531E+02, 3.237958E+02, 3.331182E+02, 3.425226E+02, 3.520113E+02,
3.615863E+02, 3.712495E+02, 3.810028E+02, 3.908478E+02, 4.007862E+02, 4.108194E+02,
4.209489E+02, 4.311760E+02, 4.415019E+02, 4.519278E+02, 4.624548E+02, 4.730840E+02,
4.838163E+02, 4.946525E+02, 5.055937E+02, 5.166406E+02, 5.277939E+02, 5.390545E+02,
5.504229E+02, 5.618998E+02, 5.734859E+02, 5.851816E+02, 5.969876E+02, 6.089044E+02,
6.209324E+02, 6.330722E+02, 6.453241E+02, 6.576887E+02, 6.701662E+02, 6.827571E+02,
6.954618E+02, 7.082806E+02, 7.212138E+02, 7.342617E+02, 7.474247E+02, 7.607031E+02,
7.740971E+02, 7.876070E+02, 8.012330E+02, 8.149755E+02, 8.288346E+02, 8.428107E+02,
8.569038E+02, 8.711142E+02, 8.854422E+02, 8.998879E+02, 9.144515E+02, 9.291333E+02,
9.439334E+02, 9.588519E+02, 9.738891E+02, 9.890452E+02, 1.004320E+03, 1.019714E+03,
1.035228E+03, 1.050861E+03, 1.066613E+03, 1.082486E+03, 1.098478E+03, 1.114590E+03,
1.130822E+03, 1.147175E+03, 1.163648E+03, 1.180242E+03, 1.196956E+03, 1.213792E+03,
1.230748E+03, 1.247825E+03, 1.265023E+03, 1.282343E+03, 1.299784E+03, 1.317346E+03,
1.335031E+03, 1.352836E+03, 1.370764E+03, 1.388814E+03, 1.406986E+03, 1.425280E+03,
1.443696E+03, 1.462234E+03, 1.480895E+03, 1.499679E+03, 1.518585E+03, 1.537614E+03,
1.556766E+03, 1.576041E+03, 1.595439E+03, 1.614960E+03, 1.634605E+03, 1.654372E+03,
1.674264E+03, 1.694278E+03, 1.714417E+03, 1.734679E+03, 1.755065E+03, 1.775574E+03,
1.796208E+03, 1.816966E+03, 1.837848E+03, 1.858855E+03, 1.879985E+03, 1.901240E+03,
1.922620E+03, 1.944124E+03, 1.965753E+03, 1.987507E+03, 2.009386E+03, 2.031390E+03,
2.053519E+03, 2.075772E+03, 2.098152E+03, 2.120656E+03, 2.143286E+03, 2.166041E+03,
2.188922E+03, 2.211929E+03, 2.235061E+03, 2.258320E+03, 2.281704E+03, 2.305214E+03,
2.328850E+03, 2.352613E+03, 2.376502E+03, 2.400517E+03, 2.424658E+03, 2.448926E+03,
2.473321E+03, 2.497842E+03, 2.522490E+03, 2.547265E+03, 2.572167E+03, 2.597196E+03,
2.622352E+03, 2.647635E+03, 2.673046E+03, 2.698584E+03, 2.724249E+03, 2.750042E+03,
2.775962E+03, 2.802011E+03, 2.828187E+03, 2.854490E+03, 2.880922E+03, 2.907482E+03,
2.934170E+03, 2.960986E+03, 2.987930E+03, 3.015003E+03, 3.042204E+03, 3.069534E+03,
3.096992E+03, 3.124579E+03, 3.152295E+03, 3.180140E+03, 3.208113E+03, 3.236216E+03,
3.264448E+03, 3.292809E+03, 3.321299E+03, 3.349918E+03, 3.378667E+03, 3.407546E+03,
3.436554E+03, 3.465692E+03, 3.494959E+03, 3.524357E+03, 3.553884E+03, 3.583542E+03,
3.613329E+03, 3.643247E+03, 3.673295E+03, 3.703473E+03, 3.733781E+03, 3.764221E+03,
3.794790E+03, 3.825491E+03, 3.856322E+03, 3.887284E+03, 3.918377E+03, 3.949600E+03,
3.980955E+03, 4.012441E+03, 4.044059E+03, 4.075807E+03, 4.107687E+03, 4.139699E+03,
4.171842E+03, 4.204116E+03, 4.236523E+03, 4.269061E+03, 4.301731E+03, 4.334532E+03,
4.367466E+03, 4.400532E+03, 4.433731E+03, 4.467061E+03, 4.500524E+03, 4.534119E+03,
4.567847E+03, 4.601707E+03, 4.635700E+03, 4.669825E+03, 4.704084E+03, 4.738475E+03,
4.772999E+03, 4.807656E+03, 4.842447E+03, 4.877370E+03, 4.912427E+03, 4.947617E+03,
4.982940E+03, 5.018397E+03, 5.053988E+03, 5.089712E+03, 5.125570E+03, 5.161561E+03,
5.197686E+03, 5.233946E+03, 5.270339E+03, 5.306866E+03, 5.343528E+03, 5.380323E+03,
5.417253E+03, 5.454318E+03, 5.491516E+03, 5.528849E+03, 5.566317E+03, 5.603919E+03,
5.641656E+03, 5.679528E+03, 5.717535E+03, 5.755676E+03, 5.793953E+03, 5.832364E+03,
5.870911E+03, 5.909592E+03, 5.948409E+03, 5.987362E+03, 6.026449E+03, 6.065672E+03,
6.105031E+03, 6.144525E+03, 6.184155E+03, 6.223920E+03, 6.263821E+03, 6.303858E+03,
6.344031E+03, 6.384340E+03, 6.424784E+03, 6.465365E+03, 6.506082E+03, 6.546935E+03,
6.587924E+03, 6.629050E+03, 6.670312E+03, 6.711710E+03, 6.753245E+03, 6.794916E+03,
6.836724E+03, 6.878669E+03, 6.920750E+03, 6.962968E+03, 7.005323E+03, 7.047814E+03,
7.090443E+03, 7.133208E+03, 7.176111E+03, 7.219151E+03, 7.262327E+03, 7.305641E+03,
7.349092E+03, 7.392681E+03, 7.436407E+03, 7.480270E+03, 7.524270E+03, 7.568408E+03,
7.612684E+03, 7.657097E+03, 7.701647E+03, 7.746336E+03, 7.791162E+03, 7.836126E+03,
7.881227E+03, 7.926467E+03, 7.971844E+03, 8.017359E+03, 8.063013E+03, 8.108804E+03,
8.154733E+03, 8.200801E+03, 8.247006E+03, 8.293350E+03, 8.339832E+03, 8.386452E+03,
8.433210E+03, 8.480107E+03, 8.527142E+03, 8.574316E+03, 8.621628E+03, 8.669078E+03,
8.716667E+03, 8.764395E+03, 8.812261E+03, 8.860266E+03, 8.908409E+03, 8.956691E+03,
9.005112E+03, 9.053671E+03, 9.102369E+03, 9.151206E+03, 9.200182E+03, 9.249297E+03,
9.298551E+03, 9.347943E+03, 9.397474E+03, 9.447145E+03, 9.496954E+03, 9.546902E+03,
9.596990E+03, 9.647216E+03, 9.697582E+03, 9.748087E+03, 9.798730E+03, 9.849513E+03,
9.900435E+03, 9.951496E+03, 1.000270E+04, 1.005404E+04, 1.010552E+04, 1.015713E+04,
1.020889E+04, 1.026079E+04, 1.031282E+04, 1.036500E+04, 1.041731E+04, 1.046977E+04,
1.052236E+04, 1.057509E+04, 1.062797E+04, 1.068098E+04, 1.073413E+04, 1.078742E+04,
1.084085E+04, 1.089442E+04, 1.094813E+04, 1.100198E+04, 1.105596E+04, 1.111009E+04,
1.116436E+04, 1.121876E+04, 1.127331E+04, 1.132799E+04, 1.138282E+04, 1.143778E+04,
1.149289E+04, 1.154813E+04, 1.160351E+04, 1.165903E+04, 1.171470E+04, 1.177050E+04,
1.182644E+04, 1.188252E+04, 1.193874E+04, 1.199510E+04, 1.205159E+04, 1.210823E+04,
1.216501E+04, 1.222193E+04, 1.227898E+04, 1.233618E+04, 1.239351E+04, 1.245099E+04,
1.250860E+04, 1.256636E+04, 1.262425E+04, 1.268228E+04, 1.274045E+04, 1.279876E+04,
1.285722E+04, 1.291581E+04, 1.297453E+04, 1.303340E+04, 1.309241E+04, 1.315156E+04,
1.321084E+04, 1.327027E+04, 1.332983E+04, 1.338954E+04, 1.344938E+04, 1.350936E+04,
1.356949E+04, 1.362975E+04, 1.369015E+04, 1.375069E+04, 1.381136E+04, 1.387218E+04,
1.393314E+04, 1.399423E+04, 1.405547E+04, 1.411684E+04, 1.417835E+04, 1.424000E+04,
1.430179E+04, 1.436372E+04, 1.442579E+04, 1.448799E+04, 1.455034E+04, 1.461282E+04,
1.467545E+04,
])
# ---------------------- M = 5, I = 9 ---------------------------
M = 5
I = 9
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[3]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
6.128630E+00, 5.062329E+01, 9.917483E+01, 1.477481E+02, 1.963300E+02, 2.449180E+02,
2.935114E+02, 3.421098E+02, 3.907129E+02, 4.393208E+02, 4.879334E+02, 5.365511E+02,
5.851749E+02, 6.338065E+02, 6.824492E+02, 7.311080E+02, 7.797902E+02, 8.285052E+02,
8.772651E+02, 9.260840E+02, 9.749786E+02, 1.023967E+03, 1.073070E+03, 1.122307E+03,
1.171702E+03, 1.221277E+03, 1.271055E+03, 1.321059E+03, 1.371312E+03, 1.421836E+03,
1.472653E+03, 1.523784E+03, 1.575250E+03, 1.627071E+03, 1.679265E+03, 1.731850E+03,
1.784843E+03, 1.838260E+03, 1.892117E+03, 1.946428E+03, 2.001207E+03, 2.056467E+03,
2.112219E+03, 2.168475E+03, 2.225246E+03, 2.282542E+03, 2.340373E+03, 2.398746E+03,
2.457671E+03, 2.517156E+03, 2.577207E+03, 2.637832E+03, 2.699037E+03, 2.760829E+03,
2.823213E+03, 2.886195E+03, 2.949779E+03, 3.013971E+03, 3.078775E+03, 3.144196E+03,
3.210237E+03, 3.276902E+03, 3.344196E+03, 3.412121E+03, 3.480680E+03, 3.549878E+03,
3.619716E+03, 3.690198E+03, 3.761326E+03, 3.833102E+03, 3.905530E+03, 3.978611E+03,
4.052348E+03, 4.126742E+03, 4.201796E+03, 4.277511E+03, 4.353890E+03, 4.430934E+03,
4.508645E+03, 4.587024E+03, 4.666073E+03, 4.745793E+03, 4.826187E+03, 4.907254E+03,
4.988997E+03, 5.071417E+03, 5.154515E+03, 5.238293E+03, 5.322751E+03, 5.407890E+03,
5.493712E+03, 5.580218E+03, 5.667408E+03, 5.755285E+03, 5.843848E+03, 5.933099E+03,
6.023038E+03, 6.113667E+03, 6.204986E+03, 6.296996E+03, 6.389699E+03, 6.483094E+03,
6.577183E+03, 6.671967E+03, 6.767446E+03, 6.863621E+03, 6.960492E+03, 7.058061E+03,
7.156329E+03, 7.255295E+03, 7.354961E+03, 7.455327E+03, 7.556394E+03, 7.658162E+03,
7.760633E+03, 7.863807E+03, 7.967684E+03, 8.072266E+03, 8.177552E+03, 8.283544E+03,
8.390242E+03, 8.497647E+03, 8.605758E+03, 8.714578E+03, 8.824106E+03, 8.934343E+03,
9.045289E+03, 9.156946E+03, 9.269313E+03, 9.382392E+03, 9.496182E+03, 9.610685E+03,
9.725901E+03, 9.841830E+03, 9.958473E+03, 1.007583E+04, 1.019390E+04, 1.031269E+04,
1.043220E+04, 1.055242E+04, 1.067336E+04, 1.079501E+04, 1.091739E+04, 1.104048E+04,
1.116429E+04, 1.128882E+04, 1.141408E+04, 1.154005E+04, 1.166674E+04, 1.179416E+04,
1.192230E+04, 1.205116E+04, 1.218075E+04, 1.231106E+04, 1.244209E+04, 1.257385E+04,
1.270633E+04, 1.283954E+04, 1.297348E+04, 1.310814E+04, 1.324353E+04, 1.337965E+04,
1.351649E+04, 1.365407E+04, 1.379237E+04, 1.393140E+04, 1.407117E+04, 1.421166E+04,
1.435289E+04, 1.449485E+04, 1.463754E+04, 1.478096E+04, 1.492512E+04, 1.507001E+04,
1.521563E+04, 1.536199E+04, 1.550909E+04, 1.565692E+04, 1.580548E+04, 1.595479E+04,
1.610483E+04, 1.625560E+04, 1.640712E+04, 1.655937E+04, 1.671237E+04, 1.686610E+04,
1.702057E+04, 1.717579E+04, 1.733174E+04, 1.748844E+04, 1.764588E+04, 1.780406E+04,
1.796298E+04, 1.812265E+04, 1.828306E+04, 1.844421E+04, 1.860611E+04, 1.876876E+04,
1.893215E+04, 1.909629E+04, 1.926117E+04, 1.942680E+04, 1.959318E+04, 1.976031E+04,
1.992819E+04, 2.009681E+04, 2.026619E+04, 2.043631E+04, 2.060719E+04, 2.077882E+04,
2.095120E+04, 2.112433E+04, 2.129821E+04, 2.147285E+04, 2.164824E+04, 2.182438E+04,
2.200128E+04, 2.217893E+04, 2.235734E+04, 2.253651E+04, 2.271643E+04, 2.289711E+04,
2.307854E+04, 2.326073E+04, 2.344368E+04, 2.362739E+04, 2.381186E+04, 2.399709E+04,
2.418308E+04, 2.436983E+04, 2.455734E+04, 2.474561E+04, 2.493464E+04, 2.512444E+04,
2.531500E+04, 2.550632E+04, 2.569841E+04, 2.589126E+04, 2.608487E+04, 2.627925E+04,
2.647440E+04, 2.667031E+04, 2.686699E+04, 2.706444E+04, 2.726265E+04, 2.746163E+04,
2.766138E+04, 2.786190E+04, 2.806319E+04, 2.826524E+04, 2.846807E+04, 2.867167E+04,
2.887604E+04, 2.908118E+04, 2.928709E+04, 2.949378E+04, 2.970124E+04, 2.990947E+04,
3.011847E+04, 3.032825E+04, 3.053880E+04, 3.075013E+04, 3.096224E+04, 3.117512E+04,
3.138877E+04, 3.160321E+04, 3.181842E+04, 3.203441E+04, 3.225117E+04, 3.246872E+04,
3.268704E+04, 3.290614E+04, 3.312602E+04, 3.334669E+04, 3.356813E+04, 3.379035E+04,
3.401336E+04, 3.423714E+04, 3.446171E+04, 3.468706E+04, 3.491320E+04, 3.514011E+04,
3.536782E+04, 3.559630E+04, 3.582557E+04, 3.605563E+04, 3.628647E+04, 3.651809E+04,
3.675050E+04, 3.698370E+04, 3.721769E+04, 3.745246E+04, 3.768802E+04, 3.792437E+04,
3.816150E+04, 3.839943E+04, 3.863814E+04, 3.887764E+04, 3.911794E+04, 3.935902E+04,
3.960089E+04, 3.984356E+04, 4.008701E+04, 4.033126E+04, 4.057630E+04, 4.082213E+04,
4.106875E+04, 4.131617E+04, 4.156438E+04, 4.181338E+04, 4.206318E+04, 4.231377E+04,
4.256515E+04, 4.281734E+04, 4.307031E+04, 4.332408E+04, 4.357865E+04, 4.383401E+04,
4.409017E+04, 4.434713E+04, 4.460488E+04, 4.486344E+04, 4.512278E+04, 4.538293E+04,
4.564388E+04, 4.590562E+04, 4.616816E+04, 4.643150E+04, 4.669564E+04, 4.696058E+04,
4.722632E+04, 4.749286E+04, 4.776021E+04, 4.802835E+04, 4.829729E+04, 4.856703E+04,
4.883758E+04, 4.910893E+04, 4.938108E+04, 4.965403E+04, 4.992778E+04, 5.020234E+04,
5.047770E+04, 5.075386E+04, 5.103083E+04, 5.130860E+04, 5.158717E+04, 5.186655E+04,
5.214674E+04, 5.242772E+04, 5.270951E+04, 5.299211E+04, 5.327551E+04, 5.355972E+04,
5.384474E+04, 5.413056E+04, 5.441718E+04, 5.470461E+04, 5.499285E+04, 5.528189E+04,
5.557175E+04, 5.586240E+04, 5.615387E+04, 5.644614E+04, 5.673922E+04, 5.703311E+04,
5.732781E+04, 5.762331E+04, 5.791962E+04, 5.821674E+04, 5.851467E+04, 5.881341E+04,
5.911295E+04, 5.941331E+04, 5.971447E+04, 6.001644E+04, 6.031922E+04, 6.062281E+04,
6.092721E+04, 6.123242E+04, 6.153844E+04, 6.184527E+04, 6.215291E+04, 6.246136E+04,
6.277062E+04, 6.308069E+04, 6.339157E+04, 6.370326E+04, 6.401576E+04, 6.432907E+04,
6.464320E+04, 6.495813E+04, 6.527387E+04, 6.559043E+04, 6.590779E+04, 6.622597E+04,
6.654496E+04, 6.686476E+04, 6.718537E+04, 6.750679E+04, 6.782902E+04, 6.815206E+04,
6.847592E+04, 6.880058E+04, 6.912606E+04, 6.945235E+04, 6.977945E+04, 7.010736E+04,
7.043609E+04, 7.076562E+04, 7.109597E+04, 7.142713E+04, 7.175910E+04, 7.209188E+04,
7.242547E+04, 7.275988E+04, 7.309509E+04, 7.343112E+04, 7.376796E+04, 7.410561E+04,
7.444407E+04, 7.478335E+04, 7.512343E+04, 7.546433E+04, 7.580603E+04, 7.614855E+04,
7.649188E+04, 7.683602E+04, 7.718098E+04, 7.752674E+04, 7.787331E+04, 7.822070E+04,
7.856889E+04, 7.891790E+04, 7.926772E+04, 7.961835E+04, 7.996979E+04, 8.032204E+04,
8.067510E+04, 8.102897E+04, 8.138365E+04, 8.173914E+04, 8.209544E+04, 8.245255E+04,
8.281047E+04, 8.316920E+04, 8.352874E+04, 8.388909E+04, 8.425025E+04, 8.461221E+04,
8.497499E+04,
])
# ---------------------- M = 6, I = 1 ---------------------------
M = 6
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
5.000000E+00, 1.273695E+01, 3.049472E+01, 5.478766E+01, 8.367753E+01, 1.164151E+02,
1.525835E+02, 1.918873E+02, 2.341011E+02, 2.790524E+02, 3.266165E+02, 3.767179E+02,
4.293326E+02, 4.844902E+02, 5.422734E+02, 6.028155E+02, 6.662963E+02, 7.329384E+02,
8.030016E+02, 8.767796E+02, 9.545964E+02, 1.036804E+03, 1.123780E+03, 1.215929E+03,
1.313679E+03, 1.417485E+03, 1.527828E+03, 1.645218E+03, 1.770193E+03, 1.903324E+03,
2.045215E+03, 2.196504E+03, 2.357871E+03, 2.530032E+03, 2.713751E+03, 2.909836E+03,
3.119144E+03, 3.342585E+03, 3.581125E+03, 3.835788E+03, 4.107662E+03, 4.397900E+03,
4.707726E+03, 5.038440E+03, 5.391417E+03, 5.768118E+03, 6.170091E+03, 6.598978E+03,
7.056516E+03, 7.544548E+03, 8.065026E+03, 8.620015E+03, 9.211704E+03, 9.842405E+03,
1.051457E+04, 1.123079E+04, 1.199379E+04, 1.280649E+04, 1.367192E+04, 1.459334E+04,
1.557414E+04, 1.661792E+04, 1.772850E+04, 1.890986E+04, 2.016624E+04, 2.150208E+04,
2.292207E+04, 2.443115E+04, 2.603450E+04, 2.773758E+04, 2.954614E+04, 3.150651E+04,
3.355181E+04, 3.572292E+04, 3.802703E+04, 4.047168E+04, 4.306485E+04, 4.581488E+04,
4.873055E+04, 5.182111E+04, 5.509623E+04, 5.856610E+04, 6.224140E+04, 6.613336E+04,
7.025373E+04, 7.461487E+04, 7.922970E+04, 8.411182E+04, 8.927543E+04, 9.473544E+04,
1.005075E+05, 1.066079E+05, 1.130538E+05, 1.198631E+05, 1.270545E+05, 1.346478E+05,
1.426634E+05, 1.511227E+05, 1.600483E+05, 1.694635E+05, 1.793929E+05, 1.898620E+05,
2.008977E+05, 2.125277E+05, 2.247814E+05, 2.376890E+05, 2.512823E+05, 2.655945E+05,
2.806600E+05, 2.965148E+05, 3.131965E+05, 3.307442E+05, 3.491987E+05, 3.686022E+05,
3.889992E+05, 4.104356E+05, 4.329593E+05, 4.566203E+05, 4.814703E+05, 5.075634E+05,
5.349557E+05, 5.637057E+05, 5.938740E+05, 6.255236E+05, 6.587202E+05, 6.935319E+05,
7.300294E+05, 7.682862E+05, 8.083785E+05, 8.503856E+05, 8.943897E+05, 9.404760E+05,
9.887330E+05, 1.039252E+06, 1.092129E+06, 1.147463E+06, 1.205355E+06, 1.265911E+06,
1.329242E+06, 1.395461E+06, 1.464686E+06, 1.537038E+06, 1.612646E+06, 1.691638E+06,
1.774151E+06, 1.860324E+06, 1.950303E+06, 2.044237E+06, 2.142281E+06, 2.244596E+06,
2.351347E+06, 2.462706E+06, 2.578850E+06, 2.699960E+06, 2.826228E+06, 2.957846E+06,
3.095017E+06, 3.237950E+06, 3.386857E+06, 3.541963E+06, 3.703494E+06, 3.871687E+06,
4.046785E+06, 4.229040E+06, 4.418710E+06, 4.616063E+06, 4.821373E+06, 5.034924E+06,
5.257009E+06, 5.487928E+06, 5.727992E+06, 5.977520E+06, 6.236841E+06, 6.506295E+06,
6.786230E+06, 7.077006E+06,
])
# ---------------------- M = 6, I = 2 ---------------------------
M = 6
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.000001E+01, 2.547411E+01, 6.098959E+01, 1.095756E+02, 1.673556E+02, 2.328309E+02,
3.051677E+02, 3.837737E+02, 4.682005E+02, 5.581019E+02, 6.532270E+02, 7.534273E+02,
8.586439E+02, 9.689291E+02, 1.084461E+03, 1.205482E+03, 1.332363E+03, 1.465530E+03,
1.605503E+03, 1.752860E+03, 1.908229E+03, 2.072336E+03, 2.245903E+03, 2.429737E+03,
2.624664E+03, 2.831592E+03, 3.051489E+03, 3.285303E+03, 3.534174E+03, 3.799126E+03,
4.081393E+03, 4.382239E+03, 4.702959E+03, 5.044945E+03, 5.409694E+03, 5.798822E+03,
6.213891E+03, 6.656804E+03, 7.129283E+03, 7.633410E+03, 8.171277E+03, 8.745069E+03,
9.357183E+03, 1.001001E+04, 1.070636E+04, 1.144891E+04, 1.224067E+04, 1.308471E+04,
1.398443E+04, 1.494334E+04, 1.596502E+04, 1.705351E+04, 1.821287E+04, 1.944758E+04,
2.076216E+04, 2.216156E+04, 2.365078E+04, 2.523547E+04, 2.692124E+04, 2.871415E+04,
3.062057E+04, 3.264725E+04, 3.480122E+04, 3.708988E+04, 3.952125E+04, 4.210358E+04,
4.484543E+04, 4.775615E+04, 5.084522E+04, 5.412274E+04, 5.759952E+04, 6.128669E+04,
6.519606E+04, 6.933999E+04, 7.373142E+04, 7.838393E+04, 8.331199E+04, 8.853045E+04,
9.405527E+04, 9.990284E+04, 1.060906E+05, 1.126365E+05, 1.195598E+05, 1.268805E+05,
1.346193E+05, 1.427982E+05, 1.514402E+05, 1.605692E+05, 1.702104E+05, 1.803902E+05,
1.911358E+05, 2.024762E+05, 2.144415E+05, 2.270628E+05, 2.403733E+05, 2.544072E+05,
2.692002E+05, 2.847901E+05, 3.012156E+05, 3.185180E+05, 3.367395E+05, 3.559247E+05,
3.761201E+05, 3.973740E+05, 4.197368E+05, 4.432611E+05, 4.680017E+05, 4.940161E+05,
5.213635E+05, 5.501059E+05, 5.803083E+05, 6.120376E+05, 6.453640E+05, 6.803606E+05,
7.171028E+05, 7.556704E+05, 7.961449E+05, 8.386119E+05, 8.831604E+05, 9.298828E+05,
9.788752E+05, 1.030238E+06, 1.084073E+06, 1.140491E+06, 1.199602E+06, 1.261522E+06,
1.326373E+06, 1.394281E+06, 1.465374E+06, 1.539790E+06, 1.617667E+06, 1.699151E+06,
1.784394E+06, 1.873552E+06, 1.966787E+06, 2.064268E+06, 2.166169E+06, 2.272673E+06,
2.383965E+06, 2.500240E+06, 2.621701E+06, 2.748556E+06, 2.881020E+06, 3.019317E+06,
3.163680E+06, 3.314348E+06, 3.471569E+06, 3.635602E+06, 3.806711E+06, 3.985172E+06,
4.171270E+06, 4.365299E+06, 4.567564E+06, 4.778380E+06, 4.998073E+06, 5.226979E+06,
5.465447E+06, 5.713837E+06, 5.972520E+06, 6.241880E+06, 6.522314E+06, 6.814234E+06,
7.118060E+06, 7.434232E+06, 7.763201E+06, 8.105433E+06, 8.461408E+06, 8.831625E+06,
9.216596E+06, 9.616850E+06, 1.003293E+07, 1.046541E+07, 1.091485E+07, 1.138187E+07,
1.186708E+07, 1.237112E+07,
])
# ---------------------- M = 6, I = 3 ---------------------------
M = 6
I = 3
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[4]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
8.136790E+00, 9.190122E+01, 2.433209E+02, 4.407898E+02, 6.744479E+02, 9.391965E+02,
1.231752E+03, 1.549735E+03, 1.891357E+03, 2.255315E+03, 2.640780E+03, 3.047413E+03,
3.475380E+03, 3.925354E+03, 4.398488E+03, 4.896378E+03, 5.421024E+03, 5.974778E+03,
6.560313E+03, 7.180591E+03, 7.838835E+03, 8.538523E+03, 9.283379E+03, 1.007737E+04,
1.092472E+04, 1.182991E+04, 1.279770E+04, 1.383315E+04, 1.494161E+04, 1.612880E+04,
1.740075E+04, 1.876392E+04, 2.022515E+04, 2.179171E+04, 2.347136E+04, 2.527233E+04,
2.720338E+04, 2.927384E+04, 3.149364E+04, 3.387330E+04, 3.642407E+04, 3.915786E+04,
4.208734E+04, 4.522600E+04, 4.858815E+04, 5.218897E+04, 5.604463E+04, 6.017224E+04,
6.458999E+04, 6.931716E+04, 7.437420E+04, 7.978279E+04, 8.556591E+04, 9.174789E+04,
9.835451E+04, 1.054131E+05, 1.129524E+05, 1.210032E+05, 1.295976E+05, 1.387699E+05,
1.485562E+05, 1.589946E+05, 1.701255E+05, 1.819914E+05, 1.946373E+05, 2.081105E+05,
2.224612E+05, 2.377420E+05, 2.540087E+05, 2.713198E+05, 2.897370E+05, 3.093255E+05,
3.301538E+05, 3.522940E+05, 3.758220E+05, 4.008176E+05, 4.273649E+05, 4.555521E+05,
4.854721E+05, 5.172225E+05, 5.509057E+05, 5.866294E+05, 6.245067E+05, 6.646560E+05,
7.072021E+05, 7.522754E+05, 8.000130E+05, 8.505587E+05, 9.040628E+05, 9.606834E+05,
1.020586E+06, 1.083943E+06, 1.150937E+06, 1.221758E+06, 1.296603E+06, 1.375682E+06,
1.459212E+06, 1.547420E+06, 1.640545E+06, 1.738836E+06, 1.842553E+06, 1.951967E+06,
2.067363E+06, 2.189037E+06, 2.317299E+06, 2.452473E+06, 2.594895E+06, 2.744919E+06,
2.902912E+06, 3.069257E+06, 3.244354E+06, 3.428620E+06, 3.622489E+06, 3.826415E+06,
4.040869E+06, 4.266343E+06, 4.503349E+06, 4.752420E+06, 5.014112E+06, 5.289002E+06,
5.577692E+06, 5.880808E+06, 6.199001E+06, 6.532948E+06, 6.883354E+06, 7.250950E+06,
7.636499E+06, 8.040792E+06, 8.464650E+06, 8.908929E+06, 9.374515E+06, 9.862331E+06,
1.037333E+07, 1.090852E+07, 1.146891E+07, 1.205559E+07, 1.266966E+07, 1.331228E+07,
1.398464E+07, 1.468798E+07, 1.542360E+07, 1.619281E+07, 1.699702E+07, 1.783764E+07,
1.871616E+07, 1.963413E+07, 2.059313E+07, 2.159483E+07, 2.264092E+07, 2.373319E+07,
2.487345E+07, 2.606363E+07, 2.730567E+07, 2.860161E+07, 2.995356E+07, 3.136369E+07,
3.283426E+07, 3.436760E+07, 3.596611E+07, 3.763229E+07, 3.936871E+07, 4.117804E+07,
4.306303E+07, 4.502651E+07, 4.707144E+07, 4.920084E+07, 5.141786E+07, 5.372573E+07,
5.612780E+07, 5.862752E+07, 6.122847E+07, 6.393432E+07, 6.674887E+07, 6.967606E+07,
7.271992E+07, 7.588465E+07, 7.917453E+07, 8.259403E+07, 8.614773E+07, 8.984036E+07,
9.367680E+07, 9.766207E+07, 1.018014E+08, 1.061000E+08, 1.105636E+08, 1.151977E+08,
1.200082E+08, 1.250011E+08, 1.301826E+08, 1.355593E+08, 1.411375E+08, 1.469242E+08,
1.529262E+08, 1.591509E+08, 1.656056E+08, 1.722979E+08, 1.792357E+08, 1.864271E+08,
1.938803E+08, 2.016041E+08, 2.096070E+08, 2.178983E+08, 2.264872E+08, 2.353833E+08,
2.445965E+08, 2.541369E+08, 2.640150E+08, 2.742414E+08, 2.848271E+08, 2.957836E+08,
3.071224E+08, 3.188555E+08, 3.309952E+08, 3.435542E+08, 3.565454E+08, 3.699822E+08,
3.838782E+08, 3.982476E+08, 4.131049E+08, 4.284647E+08, 4.443425E+08, 4.607539E+08,
4.777149E+08, 4.952421E+08, 5.133524E+08, 5.320631E+08,
])
# ---------------------- M = 6, I = 4 ---------------------------
M = 6
I = 4
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.626998E+01, 1.839479E+02, 4.870414E+02, 8.823115E+02, 1.350022E+03, 1.879967E+03,
2.465569E+03, 3.102094E+03, 3.785903E+03, 4.514479E+03, 5.286106E+03, 6.100196E+03,
6.957045E+03, 7.858055E+03, 8.805535E+03, 9.802766E+03, 1.085371E+04, 1.196313E+04,
1.313628E+04, 1.437935E+04, 1.569859E+04, 1.710107E+04, 1.859418E+04, 2.018607E+04,
2.188496E+04, 2.370019E+04, 2.564086E+04, 2.771745E+04, 2.994063E+04, 3.232161E+04,
3.487298E+04, 3.760723E+04, 4.053859E+04, 4.368088E+04, 4.705047E+04, 5.066330E+04,
5.453713E+04, 5.869100E+04, 6.314418E+04, 6.791804E+04, 7.303545E+04, 7.852005E+04,
8.439702E+04, 9.069327E+04, 9.743844E+04, 1.046622E+05, 1.123971E+05, 1.206774E+05,
1.295392E+05, 1.390219E+05, 1.491664E+05, 1.600155E+05, 1.716162E+05, 1.840160E+05,
1.972679E+05, 2.114260E+05, 2.265476E+05, 2.426952E+05, 2.599326E+05, 2.783288E+05,
2.979556E+05, 3.188895E+05, 3.412118E+05, 3.650082E+05, 3.903670E+05, 4.173853E+05,
4.461617E+05, 4.768023E+05, 5.094192E+05, 5.441294E+05, 5.810569E+05, 6.203311E+05,
6.620900E+05, 7.064783E+05, 7.536471E+05, 8.037577E+05, 8.569763E+05, 9.134819E+05,
9.734592E+05, 1.037104E+06, 1.104621E+06, 1.176227E+06, 1.252147E+06, 1.332619E+06,
1.417893E+06, 1.508228E+06, 1.603903E+06, 1.705201E+06, 1.812425E+06, 1.925893E+06,
2.045933E+06, 2.172894E+06, 2.307139E+06, 2.449047E+06, 2.599017E+06, 2.757465E+06,
2.924828E+06, 3.101559E+06, 3.288137E+06, 3.485058E+06, 3.692845E+06, 3.912041E+06,
4.143216E+06, 4.386961E+06, 4.643897E+06, 4.914672E+06, 5.199959E+06, 5.500466E+06,
5.816930E+06, 6.150113E+06, 6.500818E+06, 6.869878E+06, 7.258162E+06, 7.666580E+06,
8.096072E+06, 8.547624E+06, 9.022260E+06, 9.521045E+06, 1.004509E+07, 1.059556E+07,
1.117365E+07, 1.178061E+07, 1.241774E+07, 1.308641E+07, 1.378802E+07, 1.452403E+07,
1.529597E+07, 1.610542E+07, 1.695402E+07, 1.784349E+07, 1.877560E+07, 1.975219E+07,
2.077518E+07, 2.184655E+07, 2.296837E+07, 2.414278E+07, 2.537200E+07, 2.665835E+07,
2.800420E+07, 2.941204E+07, 3.088444E+07, 3.242407E+07, 3.403370E+07, 3.571618E+07,
3.747450E+07, 3.931172E+07, 4.123103E+07, 4.323575E+07, 4.532928E+07, 4.751517E+07,
4.979709E+07, 5.217884E+07, 5.466433E+07, 5.725764E+07, 5.996298E+07, 6.278469E+07,
6.572729E+07, 6.879543E+07, 7.199392E+07, 7.532775E+07, 7.880208E+07, 8.242220E+07,
8.619364E+07, 9.012207E+07, 9.421338E+07, 9.847362E+07, 1.029091E+08, 1.075262E+08,
1.123317E+08, 1.173325E+08, 1.225357E+08, 1.279487E+08, 1.335791E+08, 1.394346E+08,
1.455235E+08, 1.518540E+08,
])
# ---------------------- M = 7, I = 1 ---------------------------
M = 7
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[5]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.259272E+00, 1.541160E+01, 2.984283E+01, 4.432568E+01, 5.882297E+01, 7.332720E+01,
8.783584E+01, 1.023478E+02, 1.168627E+02, 1.313804E+02, 1.459015E+02, 1.604275E+02,
1.749609E+02, 1.895058E+02, 2.040679E+02, 2.186540E+02, 2.332724E+02, 2.479320E+02,
2.626424E+02, 2.774132E+02, 2.922540E+02, 3.071739E+02, 3.221816E+02, 3.372850E+02,
3.524912E+02, 3.678063E+02, 3.832358E+02, 3.987841E+02, 4.144551E+02, 4.302515E+02,
4.461757E+02, 4.622292E+02, 4.784130E+02, 4.947276E+02, 5.111729E+02, 5.277485E+02,
5.444538E+02, 5.612875E+02, 5.782484E+02, 5.953349E+02, 6.125453E+02, 6.298776E+02,
6.473299E+02, 6.649000E+02, 6.825859E+02, 7.003851E+02, 7.182956E+02, 7.363149E+02,
7.544408E+02, 7.726710E+02, 7.910032E+02, 8.094352E+02, 8.279648E+02, 8.465897E+02,
8.653078E+02, 8.841171E+02, 9.030155E+02, 9.220009E+02, 9.410715E+02, 9.602254E+02,
9.794607E+02, 9.987757E+02, 1.018169E+03, 1.037638E+03, 1.057182E+03, 1.076799E+03,
1.096488E+03, 1.116247E+03, 1.136075E+03, 1.155971E+03, 1.175934E+03, 1.195961E+03,
1.216053E+03, 1.236207E+03, 1.256424E+03, 1.276701E+03, 1.297039E+03, 1.317435E+03,
1.337890E+03, 1.358403E+03, 1.378972E+03, 1.399598E+03, 1.420279E+03, 1.441014E+03,
1.461804E+03, 1.482647E+03, 1.503544E+03, 1.524493E+03, 1.545494E+03, 1.566547E+03,
1.587651E+03, 1.608806E+03, 1.630012E+03, 1.651268E+03, 1.672575E+03, 1.693931E+03,
1.715336E+03, 1.736791E+03, 1.758295E+03, 1.779848E+03, 1.801450E+03, 1.823100E+03,
1.844800E+03, 1.866547E+03, 1.888343E+03, 1.910188E+03, 1.932080E+03, 1.954021E+03,
1.976010E+03, 1.998048E+03, 2.020133E+03, 2.042267E+03, 2.064449E+03, 2.086680E+03,
2.108959E+03, 2.131286E+03, 2.153662E+03, 2.176086E+03, 2.198559E+03, 2.221081E+03,
2.243651E+03, 2.266271E+03, 2.288939E+03, 2.311657E+03, 2.334424E+03, 2.357240E+03,
2.380105E+03, 2.403021E+03, 2.425986E+03, 2.449001E+03, 2.472066E+03, 2.495181E+03,
2.518346E+03, 2.541562E+03, 2.564828E+03, 2.588145E+03, 2.611513E+03, 2.634932E+03,
2.658402E+03, 2.681924E+03, 2.705497E+03, 2.729122E+03, 2.752798E+03, 2.776526E+03,
2.800307E+03, 2.824140E+03, 2.848025E+03, 2.871962E+03, 2.895953E+03, 2.919996E+03,
2.944092E+03, 2.968242E+03, 2.992444E+03, 3.016701E+03, 3.041010E+03, 3.065374E+03,
3.089791E+03, 3.114262E+03, 3.138788E+03, 3.163367E+03, 3.188002E+03, 3.212690E+03,
3.237434E+03, 3.262232E+03, 3.287085E+03, 3.311993E+03, 3.336957E+03, 3.361975E+03,
3.387050E+03, 3.412179E+03, 3.437365E+03, 3.462606E+03, 3.487903E+03, 3.513256E+03,
3.538665E+03, 3.564130E+03, 3.589652E+03, 3.615230E+03, 3.640865E+03, 3.666556E+03,
3.692304E+03, 3.718109E+03, 3.743970E+03, 3.769889E+03, 3.795864E+03, 3.821897E+03,
3.847987E+03, 3.874134E+03, 3.900338E+03, 3.926600E+03, 3.952920E+03, 3.979297E+03,
4.005731E+03, 4.032224E+03, 4.058774E+03, 4.085382E+03, 4.112047E+03, 4.138771E+03,
4.165553E+03, 4.192392E+03, 4.219290E+03, 4.246246E+03, 4.273260E+03, 4.300332E+03,
4.327463E+03, 4.354651E+03, 4.381898E+03, 4.409204E+03, 4.436567E+03, 4.463989E+03,
4.491470E+03, 4.519009E+03, 4.546606E+03, 4.574262E+03, 4.601977E+03, 4.629750E+03,
4.657581E+03, 4.685471E+03, 4.713419E+03, 4.741426E+03, 4.769492E+03, 4.797616E+03,
4.825799E+03, 4.854040E+03, 4.882340E+03, 4.910698E+03, 4.939115E+03, 4.967591E+03,
4.996125E+03, 5.024717E+03, 5.053368E+03, 5.082078E+03, 5.110846E+03, 5.139672E+03,
5.168557E+03, 5.197500E+03, 5.226502E+03, 5.255562E+03, 5.284680E+03, 5.313857E+03,
5.343091E+03, 5.372384E+03, 5.401736E+03, 5.431145E+03, 5.460613E+03, 5.490138E+03,
5.519722E+03, 5.549364E+03, 5.579063E+03, 5.608821E+03, 5.638636E+03, 5.668509E+03,
5.698440E+03, 5.728429E+03, 5.758475E+03, 5.788579E+03, 5.818741E+03, 5.848960E+03,
5.879236E+03, 5.909570E+03, 5.939961E+03, 5.970409E+03, 6.000915E+03, 6.031477E+03,
6.062097E+03, 6.092774E+03, 6.123507E+03, 6.154298E+03, 6.185145E+03, 6.216049E+03,
6.247009E+03, 6.278026E+03, 6.309100E+03, 6.340229E+03, 6.371416E+03, 6.402658E+03,
6.433957E+03, 6.465311E+03, 6.496722E+03, 6.528188E+03, 6.559710E+03, 6.591288E+03,
6.622922E+03, 6.654611E+03, 6.686355E+03, 6.718155E+03, 6.750010E+03, 6.781920E+03,
6.813886E+03, 6.845906E+03, 6.877981E+03, 6.910111E+03, 6.942296E+03, 6.974535E+03,
7.006828E+03, 7.039176E+03, 7.071578E+03, 7.104035E+03, 7.136545E+03, 7.169109E+03,
7.201728E+03, 7.234399E+03, 7.267125E+03, 7.299904E+03, 7.332736E+03, 7.365622E+03,
7.398560E+03, 7.431552E+03, 7.464597E+03, 7.497694E+03, 7.530844E+03, 7.564047E+03,
7.597302E+03, 7.630610E+03, 7.663969E+03, 7.697381E+03, 7.730845E+03, 7.764360E+03,
7.797928E+03, 7.831547E+03, 7.865217E+03, 7.898939E+03, 7.932712E+03, 7.966535E+03,
8.000410E+03, 8.034336E+03, 8.068313E+03, 8.102340E+03, 8.136417E+03, 8.170545E+03,
8.204723E+03, 8.238951E+03, 8.273228E+03, 8.307556E+03, 8.341933E+03, 8.376360E+03,
8.410836E+03, 8.445361E+03, 8.479935E+03, 8.514559E+03, 8.549231E+03, 8.583951E+03,
8.618721E+03, 8.653538E+03, 8.688404E+03, 8.723318E+03, 8.758280E+03, 8.793289E+03,
8.828347E+03, 8.863452E+03, 8.898604E+03, 8.933803E+03, 8.969050E+03, 9.004343E+03,
9.039683E+03, 9.075070E+03, 9.110503E+03, 9.145983E+03, 9.181509E+03, 9.217080E+03,
9.252698E+03, 9.288362E+03, 9.324071E+03, 9.359825E+03, 9.395625E+03, 9.431470E+03,
9.467359E+03, 9.503294E+03, 9.539273E+03, 9.575297E+03, 9.611365E+03, 9.647478E+03,
9.683634E+03, 9.719834E+03, 9.756078E+03, 9.792366E+03,
])
# ---------------------- M = 7, I = 2 ---------------------------
M = 7
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[5]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
3.562445E+00, 3.092123E+01, 6.151168E+01, 9.218904E+01, 1.228922E+02, 1.536083E+02,
1.843329E+02, 2.150641E+02, 2.458012E+02, 2.765444E+02, 3.072954E+02, 3.380581E+02,
3.688395E+02, 3.996500E+02, 4.305039E+02, 4.614188E+02, 4.924152E+02, 5.235163E+02,
5.547468E+02, 5.861323E+02, 6.176990E+02, 6.494730E+02, 6.814797E+02, 7.137437E+02,
7.462888E+02, 7.791373E+02, 8.123102E+02, 8.458274E+02, 8.797073E+02, 9.139671E+02,
9.486225E+02, 9.836883E+02, 1.019178E+03, 1.055104E+03, 1.091478E+03, 1.128311E+03,
1.165612E+03, 1.203390E+03, 1.241654E+03, 1.280411E+03, 1.319669E+03, 1.359433E+03,
1.399711E+03, 1.440506E+03, 1.481826E+03, 1.523674E+03, 1.566055E+03, 1.608973E+03,
1.652432E+03, 1.696437E+03, 1.740989E+03, 1.786093E+03, 1.831752E+03, 1.877969E+03,
1.924746E+03, 1.972085E+03, 2.019991E+03, 2.068465E+03, 2.117509E+03, 2.167125E+03,
2.217317E+03, 2.268085E+03, 2.319433E+03, 2.371362E+03, 2.423874E+03, 2.476971E+03,
2.530656E+03, 2.584929E+03, 2.639793E+03, 2.695250E+03, 2.751302E+03, 2.807950E+03,
2.865197E+03, 2.923044E+03, 2.981493E+03, 3.040546E+03, 3.100206E+03, 3.160473E+03,
3.221350E+03, 3.282838E+03, 3.344940E+03, 3.407658E+03, 3.470993E+03, 3.534948E+03,
3.599524E+03, 3.664723E+03, 3.730548E+03, 3.797000E+03, 3.864082E+03, 3.931795E+03,
4.000141E+03, 4.069124E+03, 4.138744E+03, 4.209003E+03, 4.279905E+03, 4.351450E+03,
4.423642E+03, 4.496482E+03, 4.569972E+03, 4.644115E+03, 4.718912E+03, 4.794367E+03,
4.870480E+03, 4.947254E+03, 5.024692E+03, 5.102796E+03, 5.181567E+03, 5.261008E+03,
5.341122E+03, 5.421910E+03, 5.503374E+03, 5.585517E+03, 5.668341E+03, 5.751848E+03,
5.836040E+03, 5.920920E+03, 6.006490E+03, 6.092751E+03, 6.179707E+03, 6.267359E+03,
6.355709E+03, 6.444760E+03, 6.534513E+03, 6.624971E+03, 6.716136E+03, 6.808010E+03,
6.900596E+03, 6.993894E+03, 7.087908E+03, 7.182639E+03, 7.278090E+03, 7.374263E+03,
7.471159E+03, 7.568780E+03, 7.667129E+03, 7.766208E+03, 7.866019E+03, 7.966563E+03,
8.067842E+03, 8.169859E+03, 8.272615E+03, 8.376113E+03, 8.480353E+03, 8.585339E+03,
8.691071E+03, 8.797553E+03, 8.904784E+03, 9.012769E+03, 9.121507E+03, 9.231001E+03,
9.341252E+03, 9.452263E+03, 9.564035E+03, 9.676570E+03, 9.789869E+03, 9.903935E+03,
1.001877E+04, 1.013437E+04, 1.025074E+04, 1.036789E+04, 1.048581E+04, 1.060450E+04,
1.072397E+04, 1.084422E+04, 1.096526E+04, 1.108707E+04, 1.120966E+04, 1.133304E+04,
1.145721E+04, 1.158216E+04, 1.170790E+04, 1.183443E+04, 1.196175E+04, 1.208986E+04,
1.221877E+04, 1.234847E+04, 1.247897E+04, 1.261026E+04, 1.274236E+04, 1.287525E+04,
1.300894E+04, 1.314344E+04, 1.327874E+04, 1.341484E+04, 1.355175E+04, 1.368946E+04,
1.382798E+04, 1.396731E+04, 1.410745E+04, 1.424840E+04, 1.439016E+04, 1.453273E+04,
1.467611E+04, 1.482031E+04, 1.496532E+04, 1.511115E+04, 1.525780E+04, 1.540526E+04,
1.555354E+04, 1.570264E+04, 1.585255E+04, 1.600329E+04, 1.615485E+04, 1.630723E+04,
1.646043E+04, 1.661446E+04, 1.676931E+04, 1.692498E+04, 1.708148E+04, 1.723880E+04,
1.739695E+04, 1.755593E+04, 1.771573E+04, 1.787636E+04, 1.803782E+04, 1.820010E+04,
1.836322E+04, 1.852716E+04, 1.869193E+04, 1.885754E+04, 1.902397E+04, 1.919124E+04,
1.935933E+04, 1.952826E+04, 1.969802E+04, 1.986861E+04, 2.004004E+04, 2.021229E+04,
2.038538E+04, 2.055931E+04, 2.073406E+04, 2.090965E+04, 2.108607E+04, 2.126333E+04,
2.144142E+04, 2.162034E+04, 2.180010E+04, 2.198070E+04, 2.216212E+04, 2.234438E+04,
2.252748E+04, 2.271141E+04, 2.289618E+04, 2.308177E+04, 2.326821E+04, 2.345548E+04,
2.364358E+04, 2.383251E+04, 2.402229E+04, 2.421289E+04, 2.440433E+04, 2.459660E+04,
2.478971E+04, 2.498365E+04, 2.517842E+04, 2.537403E+04, 2.557046E+04, 2.576774E+04,
2.596584E+04, 2.616477E+04, 2.636454E+04, 2.656514E+04, 2.676657E+04, 2.696883E+04,
2.717193E+04, 2.737585E+04, 2.758060E+04, 2.778618E+04, 2.799260E+04, 2.819984E+04,
2.840791E+04, 2.861680E+04, 2.882653E+04, 2.903708E+04, 2.924846E+04, 2.946066E+04,
2.967369E+04, 2.988754E+04, 3.010222E+04, 3.031773E+04, 3.053405E+04, 3.075120E+04,
3.096917E+04, 3.118797E+04, 3.140758E+04, 3.162802E+04, 3.184927E+04, 3.207135E+04,
3.229424E+04, 3.251795E+04, 3.274248E+04, 3.296783E+04, 3.319399E+04, 3.342096E+04,
3.364875E+04, 3.387736E+04, 3.410677E+04, 3.433700E+04, 3.456804E+04, 3.479989E+04,
3.503255E+04, 3.526602E+04, 3.550030E+04, 3.573539E+04, 3.597128E+04, 3.620797E+04,
3.644547E+04, 3.668378E+04, 3.692289E+04, 3.716280E+04, 3.740351E+04, 3.764502E+04,
3.788733E+04, 3.813044E+04, 3.837434E+04, 3.861904E+04, 3.886454E+04, 3.911083E+04,
3.935791E+04, 3.960579E+04, 3.985445E+04, 4.010391E+04, 4.035416E+04, 4.060519E+04,
4.085701E+04, 4.110962E+04, 4.136301E+04, 4.161719E+04, 4.187214E+04, 4.212788E+04,
4.238440E+04, 4.264170E+04, 4.289977E+04, 4.315863E+04, 4.341825E+04, 4.367866E+04,
4.393983E+04, 4.420178E+04, 4.446450E+04, 4.472798E+04, 4.499224E+04, 4.525726E+04,
4.552305E+04, 4.578960E+04, 4.605692E+04, 4.632499E+04, 4.659383E+04, 4.686343E+04,
4.713378E+04, 4.740489E+04, 4.767676E+04, 4.794938E+04, 4.822276E+04, 4.849688E+04,
4.877176E+04, 4.904738E+04, 4.932375E+04, 4.960087E+04, 4.987873E+04, 5.015733E+04,
5.043668E+04, 5.071676E+04, 5.099758E+04, 5.127915E+04, 5.156144E+04, 5.184447E+04,
5.212824E+04, 5.241273E+04, 5.269796E+04, 5.298391E+04, 5.327059E+04, 5.355800E+04,
5.384613E+04, 5.413498E+04, 5.442456E+04, 5.471485E+04,
])
# ---------------------- M = 7, I = 3 ---------------------------
M = 7
I = 3
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[5]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
2.092314E+01, 1.808102E+02, 3.594239E+02, 5.385443E+02, 7.178153E+02, 8.971617E+02,
1.076558E+03, 1.255992E+03, 1.435461E+03, 1.614965E+03, 1.794512E+03, 1.974123E+03,
2.153834E+03, 2.333703E+03, 2.513805E+03, 2.694239E+03, 2.875119E+03, 3.056572E+03,
3.238738E+03, 3.421762E+03, 3.605792E+03, 3.790976E+03, 3.977461E+03, 4.165389E+03,
4.354895E+03, 4.546108E+03, 4.739152E+03, 4.934142E+03, 5.131184E+03, 5.330380E+03,
5.531821E+03, 5.735595E+03, 5.941780E+03, 6.150451E+03, 6.361674E+03, 6.575513E+03,
6.792026E+03, 7.011265E+03, 7.233281E+03, 7.458117E+03, 7.685816E+03, 7.916417E+03,
8.149955E+03, 8.386465E+03, 8.625976E+03, 8.868517E+03, 9.114116E+03, 9.362797E+03,
9.614584E+03, 9.869498E+03, 1.012756E+04, 1.038879E+04, 1.065321E+04, 1.092083E+04,
1.119167E+04, 1.146574E+04, 1.174307E+04, 1.202366E+04, 1.230753E+04, 1.259470E+04,
1.288517E+04, 1.317895E+04, 1.347607E+04, 1.377654E+04, 1.408035E+04, 1.438754E+04,
1.469810E+04, 1.501205E+04, 1.532940E+04, 1.565016E+04, 1.597435E+04, 1.630197E+04,
1.663303E+04, 1.696755E+04, 1.730554E+04, 1.764700E+04, 1.799195E+04, 1.834041E+04,
1.869237E+04, 1.904786E+04, 1.940687E+04, 1.976944E+04, 2.013556E+04, 2.050525E+04,
2.087851E+04, 2.125537E+04, 2.163583E+04, 2.201990E+04, 2.240760E+04, 2.279894E+04,
2.319392E+04, 2.359257E+04, 2.399490E+04, 2.440090E+04, 2.481061E+04, 2.522403E+04,
2.564117E+04, 2.606205E+04, 2.648668E+04, 2.691506E+04, 2.734723E+04, 2.778317E+04,
2.822292E+04, 2.866648E+04, 2.911386E+04, 2.956508E+04, 3.002015E+04, 3.047908E+04,
3.094189E+04, 3.140859E+04, 3.187919E+04, 3.235370E+04, 3.283214E+04, 3.331452E+04,
3.380086E+04, 3.429116E+04, 3.478544E+04, 3.528371E+04, 3.578598E+04, 3.629228E+04,
3.680260E+04, 3.731697E+04, 3.783539E+04, 3.835788E+04, 3.888445E+04, 3.941511E+04,
3.994988E+04, 4.048877E+04, 4.103179E+04, 4.157894E+04, 4.213026E+04, 4.268574E+04,
4.324540E+04, 4.380925E+04, 4.437731E+04, 4.494957E+04, 4.552607E+04, 4.610680E+04,
4.669179E+04, 4.728103E+04, 4.787455E+04, 4.847235E+04, 4.907444E+04, 4.968084E+04,
5.029156E+04, 5.090661E+04, 5.152600E+04, 5.214974E+04, 5.277783E+04, 5.341030E+04,
5.404715E+04, 5.468839E+04, 5.533403E+04, 5.598409E+04, 5.663857E+04, 5.729748E+04,
5.796083E+04, 5.862863E+04, 5.930089E+04, 5.997762E+04, 6.065884E+04, 6.134454E+04,
6.203474E+04, 6.272945E+04, 6.342867E+04, 6.413242E+04, 6.484070E+04, 6.555352E+04,
6.627089E+04, 6.699282E+04, 6.771932E+04, 6.845039E+04, 6.918604E+04, 6.992629E+04,
7.067113E+04, 7.142058E+04, 7.217464E+04, 7.293332E+04, 7.369663E+04, 7.446457E+04,
7.523716E+04, 7.601439E+04, 7.679628E+04, 7.758283E+04, 7.837405E+04, 7.916995E+04,
7.997052E+04, 8.077579E+04, 8.158575E+04, 8.240040E+04, 8.321976E+04, 8.404384E+04,
8.487263E+04, 8.570614E+04, 8.654437E+04, 8.738735E+04, 8.823506E+04, 8.908751E+04,
8.994471E+04, 9.080666E+04, 9.167337E+04, 9.254484E+04, 9.342108E+04, 9.430209E+04,
9.518787E+04, 9.607843E+04, 9.697378E+04, 9.787391E+04, 9.877883E+04, 9.968855E+04,
1.006031E+05, 1.015224E+05, 1.024465E+05, 1.033754E+05, 1.043092E+05, 1.052477E+05,
1.061911E+05, 1.071393E+05, 1.080923E+05, 1.090502E+05, 1.100128E+05, 1.109803E+05,
1.119527E+05, 1.129299E+05, 1.139119E+05, 1.148987E+05, 1.158904E+05, 1.168869E+05,
1.178883E+05, 1.188945E+05, 1.199056E+05, 1.209215E+05, 1.219423E+05, 1.229679E+05,
1.239984E+05, 1.250338E+05, 1.260739E+05, 1.271190E+05, 1.281689E+05, 1.292237E+05,
1.302833E+05, 1.313477E+05, 1.324171E+05, 1.334913E+05, 1.345703E+05, 1.356543E+05,
1.367430E+05, 1.378367E+05, 1.389352E+05, 1.400385E+05, 1.411467E+05, 1.422598E+05,
1.433777E+05, 1.445005E+05, 1.456282E+05, 1.467607E+05, 1.478980E+05, 1.490402E+05,
1.501873E+05, 1.513392E+05, 1.524960E+05, 1.536576E+05, 1.548240E+05, 1.559954E+05,
1.571715E+05, 1.583525E+05, 1.595383E+05, 1.607290E+05, 1.619245E+05, 1.631249E+05,
1.643301E+05, 1.655401E+05, 1.667549E+05, 1.679746E+05, 1.691991E+05, 1.704284E+05,
1.716626E+05, 1.729015E+05, 1.741453E+05, 1.753939E+05, 1.766473E+05, 1.779055E+05,
1.791685E+05, 1.804363E+05, 1.817088E+05, 1.829862E+05, 1.842684E+05, 1.855554E+05,
1.868471E+05, 1.881436E+05, 1.894449E+05, 1.907510E+05, 1.920619E+05, 1.933775E+05,
1.946978E+05, 1.960229E+05, 1.973528E+05, 1.986874E+05, 2.000268E+05, 2.013709E+05,
2.027197E+05, 2.040733E+05, 2.054316E+05, 2.067946E+05, 2.081624E+05, 2.095348E+05,
2.109120E+05, 2.122938E+05, 2.136804E+05, 2.150716E+05, 2.164676E+05, 2.178682E+05,
2.192735E+05, 2.206834E+05, 2.220981E+05, 2.235174E+05, 2.249413E+05, 2.263699E+05,
2.278031E+05, 2.292410E+05, 2.306835E+05, 2.321307E+05, 2.335825E+05, 2.350388E+05,
2.364998E+05, 2.379654E+05, 2.394356E+05, 2.409104E+05, 2.423898E+05, 2.438737E+05,
2.453622E+05, 2.468553E+05, 2.483530E+05, 2.498552E+05, 2.513619E+05, 2.528732E+05,
2.543890E+05, 2.559094E+05, 2.574342E+05, 2.589636E+05, 2.604975E+05, 2.620359E+05,
2.635788E+05, 2.651261E+05, 2.666780E+05, 2.682343E+05, 2.697950E+05, 2.713603E+05,
2.729299E+05, 2.745041E+05, 2.760826E+05, 2.776656E+05, 2.792530E+05, 2.808448E+05,
2.824410E+05, 2.840416E+05, 2.856466E+05, 2.872560E+05, 2.888698E+05, 2.904879E+05,
2.921104E+05, 2.937372E+05, 2.953684E+05, 2.970038E+05, 2.986437E+05, 3.002878E+05,
3.019362E+05, 3.035890E+05, 3.052460E+05, 3.069073E+05, 3.085729E+05, 3.102428E+05,
3.119169E+05, 3.135953E+05, 3.152779E+05, 3.169647E+05,
])
# ---------------------- M = 7, I = 3 ---------------------------
M = 7
I = 3
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[5]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
2.092314E+01, 1.808102E+02, 3.594239E+02, 5.385443E+02, 7.178153E+02, 8.971617E+02,
1.076558E+03, 1.255992E+03, 1.435461E+03, 1.614965E+03, 1.794512E+03, 1.974123E+03,
2.153834E+03, 2.333703E+03, 2.513805E+03, 2.694239E+03, 2.875119E+03, 3.056572E+03,
3.238738E+03, 3.421762E+03, 3.605792E+03, 3.790976E+03, 3.977461E+03, 4.165389E+03,
4.354895E+03, 4.546108E+03, 4.739152E+03, 4.934142E+03, 5.131184E+03, 5.330380E+03,
5.531821E+03, 5.735595E+03, 5.941780E+03, 6.150451E+03, 6.361674E+03, 6.575513E+03,
6.792026E+03, 7.011265E+03, 7.233281E+03, 7.458117E+03, 7.685816E+03, 7.916417E+03,
8.149955E+03, 8.386465E+03, 8.625976E+03, 8.868517E+03, 9.114116E+03, 9.362797E+03,
9.614584E+03, 9.869498E+03, 1.012756E+04, 1.038879E+04, 1.065321E+04, 1.092083E+04,
1.119167E+04, 1.146574E+04, 1.174307E+04, 1.202366E+04, 1.230753E+04, 1.259470E+04,
1.288517E+04, 1.317895E+04, 1.347607E+04, 1.377654E+04, 1.408035E+04, 1.438754E+04,
1.469810E+04, 1.501205E+04, 1.532940E+04, 1.565016E+04, 1.597435E+04, 1.630197E+04,
1.663303E+04, 1.696755E+04, 1.730554E+04, 1.764700E+04, 1.799195E+04, 1.834041E+04,
1.869237E+04, 1.904786E+04, 1.940687E+04, 1.976944E+04, 2.013556E+04, 2.050525E+04,
2.087851E+04, 2.125537E+04, 2.163583E+04, 2.201990E+04, 2.240760E+04, 2.279894E+04,
2.319392E+04, 2.359257E+04, 2.399490E+04, 2.440090E+04, 2.481061E+04, 2.522403E+04,
2.564117E+04, 2.606205E+04, 2.648668E+04, 2.691506E+04, 2.734723E+04, 2.778317E+04,
2.822292E+04, 2.866648E+04, 2.911386E+04, 2.956508E+04, 3.002015E+04, 3.047908E+04,
3.094189E+04, 3.140859E+04, 3.187919E+04, 3.235370E+04, 3.283214E+04, 3.331452E+04,
3.380086E+04, 3.429116E+04, 3.478544E+04, 3.528371E+04, 3.578598E+04, 3.629228E+04,
3.680260E+04, 3.731697E+04, 3.783539E+04, 3.835788E+04, 3.888445E+04, 3.941511E+04,
3.994988E+04, 4.048877E+04, 4.103179E+04, 4.157894E+04, 4.213026E+04, 4.268574E+04,
4.324540E+04, 4.380925E+04, 4.437731E+04, 4.494957E+04, 4.552607E+04, 4.610680E+04,
4.669179E+04, 4.728103E+04, 4.787455E+04, 4.847235E+04, 4.907444E+04, 4.968084E+04,
5.029156E+04, 5.090661E+04, 5.152600E+04, 5.214974E+04, 5.277783E+04, 5.341030E+04,
5.404715E+04, 5.468839E+04, 5.533403E+04, 5.598409E+04, 5.663857E+04, 5.729748E+04,
5.796083E+04, 5.862863E+04, 5.930089E+04, 5.997762E+04, 6.065884E+04, 6.134454E+04,
6.203474E+04, 6.272945E+04, 6.342867E+04, 6.413242E+04, 6.484070E+04, 6.555352E+04,
6.627089E+04, 6.699282E+04, 6.771932E+04, 6.845039E+04, 6.918604E+04, 6.992629E+04,
7.067113E+04, 7.142058E+04, 7.217464E+04, 7.293332E+04, 7.369663E+04, 7.446457E+04,
7.523716E+04, 7.601439E+04, 7.679628E+04, 7.758283E+04, 7.837405E+04, 7.916995E+04,
7.997052E+04, 8.077579E+04, 8.158575E+04, 8.240040E+04, 8.321976E+04, 8.404384E+04,
8.487263E+04, 8.570614E+04, 8.654437E+04, 8.738735E+04, 8.823506E+04, 8.908751E+04,
8.994471E+04, 9.080666E+04, 9.167337E+04, 9.254484E+04, 9.342108E+04, 9.430209E+04,
9.518787E+04, 9.607843E+04, 9.697378E+04, 9.787391E+04, 9.877883E+04, 9.968855E+04,
1.006031E+05, 1.015224E+05, 1.024465E+05, 1.033754E+05, 1.043092E+05, 1.052477E+05,
1.061911E+05, 1.071393E+05, 1.080923E+05, 1.090502E+05, 1.100128E+05, 1.109803E+05,
1.119527E+05, 1.129299E+05, 1.139119E+05, 1.148987E+05, 1.158904E+05, 1.168869E+05,
1.178883E+05, 1.188945E+05, 1.199056E+05, 1.209215E+05, 1.219423E+05, 1.229679E+05,
1.239984E+05, 1.250338E+05, 1.260739E+05, 1.271190E+05, 1.281689E+05, 1.292237E+05,
1.302833E+05, 1.313477E+05, 1.324171E+05, 1.334913E+05, 1.345703E+05, 1.356543E+05,
1.367430E+05, 1.378367E+05, 1.389352E+05, 1.400385E+05, 1.411467E+05, 1.422598E+05,
1.433777E+05, 1.445005E+05, 1.456282E+05, 1.467607E+05, 1.478980E+05, 1.490402E+05,
1.501873E+05, 1.513392E+05, 1.524960E+05, 1.536576E+05, 1.548240E+05, 1.559954E+05,
1.571715E+05, 1.583525E+05, 1.595383E+05, 1.607290E+05, 1.619245E+05, 1.631249E+05,
1.643301E+05, 1.655401E+05, 1.667549E+05, 1.679746E+05, 1.691991E+05, 1.704284E+05,
1.716626E+05, 1.729015E+05, 1.741453E+05, 1.753939E+05, 1.766473E+05, 1.779055E+05,
1.791685E+05, 1.804363E+05, 1.817088E+05, 1.829862E+05, 1.842684E+05, 1.855554E+05,
1.868471E+05, 1.881436E+05, 1.894449E+05, 1.907510E+05, 1.920619E+05, 1.933775E+05,
1.946978E+05, 1.960229E+05, 1.973528E+05, 1.986874E+05, 2.000268E+05, 2.013709E+05,
2.027197E+05, 2.040733E+05, 2.054316E+05, 2.067946E+05, 2.081624E+05, 2.095348E+05,
2.109120E+05, 2.122938E+05, 2.136804E+05, 2.150716E+05, 2.164676E+05, 2.178682E+05,
2.192735E+05, 2.206834E+05, 2.220981E+05, 2.235174E+05, 2.249413E+05, 2.263699E+05,
2.278031E+05, 2.292410E+05, 2.306835E+05, 2.321307E+05, 2.335825E+05, 2.350388E+05,
2.364998E+05, 2.379654E+05, 2.394356E+05, 2.409104E+05, 2.423898E+05, 2.438737E+05,
2.453622E+05, 2.468553E+05, 2.483530E+05, 2.498552E+05, 2.513619E+05, 2.528732E+05,
2.543890E+05, 2.559094E+05, 2.574342E+05, 2.589636E+05, 2.604975E+05, 2.620359E+05,
2.635788E+05, 2.651261E+05, 2.666780E+05, 2.682343E+05, 2.697950E+05, 2.713603E+05,
2.729299E+05, 2.745041E+05, 2.760826E+05, 2.776656E+05, 2.792530E+05, 2.808448E+05,
2.824410E+05, 2.840416E+05, 2.856466E+05, 2.872560E+05, 2.888698E+05, 2.904879E+05,
2.921104E+05, 2.937372E+05, 2.953684E+05, 2.970038E+05, 2.986437E+05, 3.002878E+05,
3.019362E+05, 3.035890E+05, 3.052460E+05, 3.069073E+05, 3.085729E+05, 3.102428E+05,
3.119169E+05, 3.135953E+05, 3.152779E+05, 3.169647E+05,
])
# ---------------------- M = 7, I = 4 ---------------------------
M = 7
I = 4
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[5]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.270969E+00, 1.687773E+01, 3.312258E+01, 4.941808E+01, 6.572814E+01, 8.204535E+01,
9.836721E+01, 1.146927E+02, 1.310214E+02, 1.473535E+02, 1.636904E+02, 1.800346E+02,
1.963907E+02, 2.127654E+02, 2.291674E+02, 2.456073E+02, 2.620976E+02, 2.786516E+02,
2.952836E+02, 3.120083E+02, 3.288405E+02, 3.457946E+02, 3.628847E+02, 3.801244E+02,
3.975266E+02, 4.151032E+02, 4.328657E+02, 4.508247E+02, 4.689898E+02, 4.873702E+02,
5.059742E+02, 5.248095E+02, 5.438832E+02, 5.632018E+02, 5.827712E+02, 6.025970E+02,
6.226842E+02, 6.430374E+02, 6.636609E+02, 6.845586E+02, 7.057342E+02, 7.271909E+02,
7.489318E+02, 7.709599E+02, 7.932777E+02, 8.158878E+02, 8.387923E+02, 8.619935E+02,
8.854934E+02, 9.092938E+02, 9.333964E+02, 9.578031E+02, 9.825153E+02, 1.007535E+03,
1.032862E+03, 1.058500E+03, 1.084448E+03, 1.110709E+03, 1.137284E+03, 1.164173E+03,
1.191378E+03, 1.218900E+03, 1.246741E+03, 1.274900E+03, 1.303379E+03, 1.332180E+03,
1.361303E+03, 1.390749E+03, 1.420519E+03, 1.450614E+03, 1.481036E+03, 1.511784E+03,
1.542861E+03, 1.574267E+03, 1.606003E+03, 1.638069E+03, 1.670468E+03, 1.703200E+03,
1.736266E+03, 1.769667E+03, 1.803403E+03, 1.837477E+03, 1.871889E+03, 1.906639E+03,
1.941730E+03, 1.977162E+03, 2.012936E+03, 2.049053E+03, 2.085515E+03, 2.122321E+03,
2.159474E+03, 2.196974E+03, 2.234823E+03, 2.273021E+03, 2.311570E+03, 2.350471E+03,
2.389724E+03, 2.429331E+03, 2.469293E+03, 2.509612E+03, 2.550287E+03, 2.591320E+03,
2.632713E+03, 2.674467E+03, 2.716582E+03, 2.759059E+03, 2.801900E+03, 2.845107E+03,
2.888679E+03, 2.932618E+03, 2.976925E+03, 3.021602E+03, 3.066649E+03, 3.112067E+03,
3.157858E+03, 3.204022E+03, 3.250562E+03, 3.297476E+03, 3.344768E+03, 3.392438E+03,
3.440486E+03, 3.488915E+03, 3.537724E+03, 3.586916E+03, 3.636491E+03, 3.686450E+03,
3.736794E+03, 3.787524E+03, 3.838641E+03, 3.890147E+03, 3.942042E+03, 3.994327E+03,
4.047003E+03, 4.100071E+03, 4.153533E+03, 4.207388E+03, 4.261638E+03, 4.316284E+03,
4.371327E+03, 4.426768E+03, 4.482608E+03, 4.538846E+03, 4.595486E+03, 4.652526E+03,
4.709969E+03, 4.767815E+03, 4.826064E+03, 4.884718E+03, 4.943778E+03, 5.003244E+03,
5.063117E+03, 5.123398E+03, 5.184088E+03, 5.245187E+03, 5.306697E+03, 5.368617E+03,
5.430949E+03, 5.493693E+03, 5.556851E+03, 5.620423E+03, 5.684409E+03, 5.748810E+03,
5.813627E+03, 5.878861E+03, 5.944512E+03, 6.010580E+03, 6.077068E+03, 6.143974E+03,
6.211300E+03, 6.279047E+03, 6.347214E+03, 6.415803E+03, 6.484814E+03, 6.554247E+03,
6.624104E+03, 6.694384E+03, 6.765089E+03, 6.836218E+03, 6.907772E+03, 6.979752E+03,
7.052159E+03, 7.124992E+03, 7.198252E+03, 7.271939E+03, 7.346055E+03, 7.420599E+03,
7.495571E+03, 7.570973E+03, 7.646805E+03, 7.723066E+03, 7.799758E+03, 7.876881E+03,
7.954435E+03, 8.032420E+03, 8.110837E+03, 8.189686E+03, 8.268968E+03, 8.348682E+03,
8.428830E+03, 8.509410E+03, 8.590424E+03, 8.671872E+03, 8.753754E+03, 8.836071E+03,
8.918822E+03, 9.002007E+03, 9.085628E+03, 9.169684E+03, 9.254175E+03, 9.339102E+03,
9.424465E+03, 9.510263E+03, 9.596498E+03, 9.683169E+03, 9.770276E+03, 9.857820E+03,
9.945801E+03, 1.003422E+04, 1.012307E+04, 1.021236E+04, 1.030209E+04, 1.039226E+04,
1.048286E+04, 1.057390E+04, 1.066537E+04, 1.075729E+04, 1.084964E+04, 1.094243E+04,
1.103566E+04, 1.112932E+04, 1.122342E+04, 1.131796E+04, 1.141293E+04, 1.150835E+04,
1.160420E+04, 1.170048E+04, 1.179721E+04, 1.189437E+04, 1.199197E+04, 1.209001E+04,
1.218848E+04, 1.228739E+04, 1.238673E+04, 1.248652E+04, 1.258674E+04, 1.268739E+04,
1.278848E+04, 1.289001E+04, 1.299198E+04, 1.309438E+04, 1.319721E+04, 1.330048E+04,
1.340419E+04, 1.350833E+04, 1.361290E+04, 1.371791E+04, 1.382336E+04, 1.392924E+04,
1.403555E+04, 1.414230E+04, 1.424948E+04, 1.435709E+04, 1.446514E+04, 1.457362E+04,
1.468253E+04, 1.479187E+04, 1.490165E+04, 1.501185E+04, 1.512249E+04, 1.523356E+04,
1.534506E+04, 1.545699E+04, 1.556935E+04, 1.568214E+04, 1.579536E+04, 1.590901E+04,
1.602309E+04, 1.613759E+04, 1.625253E+04, 1.636789E+04, 1.648367E+04, 1.659989E+04,
1.671653E+04, 1.683360E+04, 1.695109E+04, 1.706900E+04, 1.718735E+04, 1.730611E+04,
1.742530E+04, 1.754492E+04, 1.766495E+04, 1.778541E+04, 1.790629E+04, 1.802759E+04,
1.814932E+04, 1.827146E+04, 1.839402E+04, 1.851701E+04, 1.864041E+04, 1.876423E+04,
1.888847E+04, 1.901313E+04, 1.913820E+04, 1.926369E+04, 1.938959E+04, 1.951592E+04,
1.964265E+04, 1.976980E+04, 1.989737E+04, 2.002534E+04, 2.015373E+04, 2.028253E+04,
2.041175E+04, 2.054137E+04, 2.067141E+04, 2.080185E+04, 2.093270E+04, 2.106396E+04,
2.119563E+04, 2.132771E+04, 2.146019E+04, 2.159308E+04, 2.172638E+04, 2.186007E+04,
2.199418E+04, 2.212868E+04, 2.226359E+04, 2.239890E+04, 2.253461E+04, 2.267073E+04,
2.280724E+04, 2.294415E+04, 2.308146E+04, 2.321917E+04, 2.335728E+04, 2.349578E+04,
2.363468E+04, 2.377397E+04, 2.391366E+04, 2.405374E+04, 2.419421E+04, 2.433508E+04,
2.447634E+04, 2.461799E+04, 2.476002E+04, 2.490245E+04, 2.504527E+04, 2.518847E+04,
2.533206E+04, 2.547604E+04, 2.562040E+04, 2.576515E+04, 2.591028E+04, 2.605580E+04,
2.620169E+04, 2.634797E+04, 2.649463E+04, 2.664167E+04, 2.678908E+04, 2.693688E+04,
2.708505E+04, 2.723360E+04, 2.738253E+04, 2.753183E+04, 2.768151E+04, 2.783155E+04,
2.798198E+04, 2.813277E+04, 2.828393E+04, 2.843547E+04, 2.858737E+04, 2.873964E+04,
2.889228E+04, 2.904528E+04, 2.919865E+04, 2.935239E+04,
])
# ---------------------- M = 7, I = 5 ---------------------------
M = 7
I = 5
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[5]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
2.157469E+01, 1.910172E+02, 3.804688E+02, 5.704593E+02, 7.606097E+02, 9.508401E+02,
1.141123E+03, 1.331447E+03, 1.521808E+03, 1.712208E+03, 1.902659E+03, 2.093190E+03,
2.283847E+03, 2.474702E+03, 2.665851E+03, 2.857410E+03, 3.049515E+03, 3.242316E+03,
3.435973E+03, 3.630652E+03, 3.826519E+03, 4.023741E+03, 4.222477E+03, 4.422884E+03,
4.625110E+03, 4.829294E+03, 5.035569E+03, 5.244058E+03, 5.454873E+03, 5.668122E+03,
5.883902E+03, 6.102304E+03, 6.323410E+03, 6.547298E+03, 6.774037E+03, 7.003694E+03,
7.236326E+03, 7.471990E+03, 7.710735E+03, 7.952609E+03, 8.197654E+03, 8.445909E+03,
8.697412E+03, 8.952197E+03, 9.210295E+03, 9.471735E+03, 9.736545E+03, 1.000475E+04,
1.027637E+04, 1.055144E+04, 1.082997E+04, 1.111198E+04, 1.139749E+04, 1.168652E+04,
1.197908E+04, 1.227520E+04, 1.257488E+04, 1.287815E+04, 1.318501E+04, 1.349548E+04,
1.380958E+04, 1.412731E+04, 1.444869E+04, 1.477374E+04, 1.510246E+04, 1.543486E+04,
1.577096E+04, 1.611078E+04, 1.645431E+04, 1.680158E+04, 1.715259E+04, 1.750736E+04,
1.786589E+04, 1.822821E+04, 1.859431E+04, 1.896422E+04, 1.933794E+04, 1.971548E+04,
2.009687E+04, 2.048210E+04, 2.087119E+04, 2.126415E+04, 2.166100E+04, 2.206174E+04,
2.246639E+04, 2.287496E+04, 2.328746E+04, 2.370391E+04, 2.412431E+04, 2.454868E+04,
2.497703E+04, 2.540938E+04, 2.584574E+04, 2.628611E+04, 2.673051E+04, 2.717896E+04,
2.763147E+04, 2.808804E+04, 2.854870E+04, 2.901346E+04, 2.948232E+04, 2.995531E+04,
3.043243E+04, 3.091370E+04, 3.139913E+04, 3.188873E+04, 3.238252E+04, 3.288051E+04,
3.338272E+04, 3.388915E+04, 3.439982E+04, 3.491474E+04, 3.543392E+04, 3.595739E+04,
3.648514E+04, 3.701720E+04, 3.755358E+04, 3.809428E+04, 3.863933E+04, 3.918873E+04,
3.974250E+04, 4.030065E+04, 4.086319E+04, 4.143014E+04, 4.200151E+04, 4.257730E+04,
4.315754E+04, 4.374223E+04, 4.433139E+04, 4.492502E+04, 4.552315E+04, 4.612578E+04,
4.673292E+04, 4.734459E+04, 4.796079E+04, 4.858154E+04, 4.920686E+04, 4.983674E+04,
5.047121E+04, 5.111027E+04, 5.175393E+04, 5.240221E+04, 5.305511E+04, 5.371265E+04,
5.437484E+04, 5.504169E+04, 5.571320E+04, 5.638939E+04, 5.707027E+04, 5.775584E+04,
5.844613E+04, 5.914113E+04, 5.984085E+04, 6.054532E+04, 6.125452E+04, 6.196849E+04,
6.268721E+04, 6.341071E+04, 6.413899E+04, 6.487206E+04, 6.560993E+04, 6.635261E+04,
6.710010E+04, 6.785242E+04, 6.860957E+04, 6.937155E+04, 7.013839E+04, 7.091008E+04,
7.168663E+04, 7.246805E+04, 7.325435E+04, 7.404554E+04, 7.484161E+04, 7.564259E+04,
7.644847E+04, 7.725926E+04, 7.807497E+04, 7.889561E+04, 7.972118E+04, 8.055169E+04,
8.138714E+04, 8.222754E+04, 8.307289E+04, 8.392321E+04, 8.477849E+04, 8.563874E+04,
8.650398E+04, 8.737419E+04, 8.824940E+04, 8.912959E+04, 9.001479E+04, 9.090498E+04,
9.180018E+04, 9.270040E+04, 9.360563E+04, 9.451588E+04, 9.543115E+04, 9.635146E+04,
9.727679E+04, 9.820716E+04, 9.914257E+04, 1.000830E+05, 1.010285E+05, 1.019791E+05,
1.029347E+05, 1.038953E+05, 1.048611E+05, 1.058318E+05, 1.068077E+05, 1.077886E+05,
1.087746E+05, 1.097656E+05, 1.107617E+05, 1.117630E+05, 1.127692E+05, 1.137806E+05,
1.147971E+05, 1.158186E+05, 1.168452E+05, 1.178769E+05, 1.189137E+05, 1.199556E+05,
1.210026E+05, 1.220546E+05, 1.231118E+05, 1.241741E+05, 1.252414E+05, 1.263139E+05,
1.273914E+05, 1.284741E+05, 1.295618E+05, 1.306547E+05, 1.317526E+05, 1.328556E+05,
1.339638E+05, 1.350770E+05, 1.361953E+05, 1.373188E+05, 1.384473E+05, 1.395809E+05,
1.407197E+05, 1.418635E+05, 1.430124E+05, 1.441664E+05, 1.453255E+05, 1.464897E+05,
1.476590E+05, 1.488334E+05, 1.500129E+05, 1.511974E+05, 1.523871E+05, 1.535818E+05,
1.547816E+05, 1.559865E+05, 1.571964E+05, 1.584115E+05, 1.596316E+05, 1.608568E+05,
1.620871E+05, 1.633224E+05, 1.645628E+05, 1.658083E+05, 1.670588E+05, 1.683144E+05,
1.695750E+05, 1.708407E+05, 1.721114E+05, 1.733872E+05, 1.746680E+05, 1.759539E+05,
1.772448E+05, 1.785407E+05, 1.798417E+05, 1.811477E+05, 1.824587E+05, 1.837748E+05,
1.850958E+05, 1.864219E+05, 1.877530E+05, 1.890890E+05, 1.904301E+05, 1.917762E+05,
1.931273E+05, 1.944833E+05, 1.958443E+05, 1.972104E+05, 1.985814E+05, 1.999573E+05,
2.013382E+05, 2.027241E+05, 2.041150E+05, 2.055107E+05, 2.069115E+05, 2.083171E+05,
2.097278E+05, 2.111433E+05, 2.125638E+05, 2.139891E+05, 2.154194E+05, 2.168546E+05,
2.182947E+05, 2.197397E+05, 2.211896E+05, 2.226444E+05, 2.241041E+05, 2.255686E+05,
2.270380E+05, 2.285122E+05, 2.299914E+05, 2.314753E+05, 2.329641E+05, 2.344578E+05,
2.359562E+05, 2.374595E+05, 2.389676E+05, 2.404806E+05, 2.419983E+05, 2.435208E+05,
2.450481E+05, 2.465802E+05, 2.481171E+05, 2.496587E+05, 2.512051E+05, 2.527563E+05,
2.543122E+05, 2.558729E+05, 2.574382E+05, 2.590083E+05, 2.605832E+05, 2.621627E+05,
2.637469E+05, 2.653359E+05, 2.669295E+05, 2.685278E+05, 2.701308E+05, 2.717384E+05,
2.733507E+05, 2.749677E+05, 2.765893E+05, 2.782155E+05, 2.798463E+05, 2.814818E+05,
2.831219E+05, 2.847665E+05, 2.864158E+05, 2.880697E+05, 2.897281E+05, 2.913911E+05,
2.930586E+05, 2.947307E+05, 2.964074E+05, 2.980885E+05, 2.997742E+05, 3.014644E+05,
3.031591E+05, 3.048584E+05, 3.065620E+05, 3.082702E+05, 3.099829E+05, 3.117000E+05,
3.134215E+05, 3.151475E+05, 3.168780E+05, 3.186128E+05, 3.203521E+05, 3.220958E+05,
3.238439E+05, 3.255963E+05, 3.273532E+05, 3.291144E+05, 3.308799E+05, 3.326499E+05,
3.344241E+05, 3.362027E+05, 3.379856E+05, 3.397728E+05,
])
# ---------------------- M = 7, I = 5 ---------------------------
M = 7
I = 5
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[5]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
2.157469E+01, 1.910172E+02, 3.804688E+02, 5.704593E+02, 7.606097E+02, 9.508401E+02,
1.141123E+03, 1.331447E+03, 1.521808E+03, 1.712208E+03, 1.902659E+03, 2.093190E+03,
2.283847E+03, 2.474702E+03, 2.665851E+03, 2.857410E+03, 3.049515E+03, 3.242316E+03,
3.435973E+03, 3.630652E+03, 3.826519E+03, 4.023741E+03, 4.222477E+03, 4.422884E+03,
4.625110E+03, 4.829294E+03, 5.035569E+03, 5.244058E+03, 5.454873E+03, 5.668122E+03,
5.883902E+03, 6.102304E+03, 6.323410E+03, 6.547298E+03, 6.774037E+03, 7.003694E+03,
7.236326E+03, 7.471990E+03, 7.710735E+03, 7.952609E+03, 8.197654E+03, 8.445909E+03,
8.697412E+03, 8.952197E+03, 9.210295E+03, 9.471735E+03, 9.736545E+03, 1.000475E+04,
1.027637E+04, 1.055144E+04, 1.082997E+04, 1.111198E+04, 1.139749E+04, 1.168652E+04,
1.197908E+04, 1.227520E+04, 1.257488E+04, 1.287815E+04, 1.318501E+04, 1.349548E+04,
1.380958E+04, 1.412731E+04, 1.444869E+04, 1.477374E+04, 1.510246E+04, 1.543486E+04,
1.577096E+04, 1.611078E+04, 1.645431E+04, 1.680158E+04, 1.715259E+04, 1.750736E+04,
1.786589E+04, 1.822821E+04, 1.859431E+04, 1.896422E+04, 1.933794E+04, 1.971548E+04,
2.009687E+04, 2.048210E+04, 2.087119E+04, 2.126415E+04, 2.166100E+04, 2.206174E+04,
2.246639E+04, 2.287496E+04, 2.328746E+04, 2.370391E+04, 2.412431E+04, 2.454868E+04,
2.497703E+04, 2.540938E+04, 2.584574E+04, 2.628611E+04, 2.673051E+04, 2.717896E+04,
2.763147E+04, 2.808804E+04, 2.854870E+04, 2.901346E+04, 2.948232E+04, 2.995531E+04,
3.043243E+04, 3.091370E+04, 3.139913E+04, 3.188873E+04, 3.238252E+04, 3.288051E+04,
3.338272E+04, 3.388915E+04, 3.439982E+04, 3.491474E+04, 3.543392E+04, 3.595739E+04,
3.648514E+04, 3.701720E+04, 3.755358E+04, 3.809428E+04, 3.863933E+04, 3.918873E+04,
3.974250E+04, 4.030065E+04, 4.086319E+04, 4.143014E+04, 4.200151E+04, 4.257730E+04,
4.315754E+04, 4.374223E+04, 4.433139E+04, 4.492502E+04, 4.552315E+04, 4.612578E+04,
4.673292E+04, 4.734459E+04, 4.796079E+04, 4.858154E+04, 4.920686E+04, 4.983674E+04,
5.047121E+04, 5.111027E+04, 5.175393E+04, 5.240221E+04, 5.305511E+04, 5.371265E+04,
5.437484E+04, 5.504169E+04, 5.571320E+04, 5.638939E+04, 5.707027E+04, 5.775584E+04,
5.844613E+04, 5.914113E+04, 5.984085E+04, 6.054532E+04, 6.125452E+04, 6.196849E+04,
6.268721E+04, 6.341071E+04, 6.413899E+04, 6.487206E+04, 6.560993E+04, 6.635261E+04,
6.710010E+04, 6.785242E+04, 6.860957E+04, 6.937155E+04, 7.013839E+04, 7.091008E+04,
7.168663E+04, 7.246805E+04, 7.325435E+04, 7.404554E+04, 7.484161E+04, 7.564259E+04,
7.644847E+04, 7.725926E+04, 7.807497E+04, 7.889561E+04, 7.972118E+04, 8.055169E+04,
8.138714E+04, 8.222754E+04, 8.307289E+04, 8.392321E+04, 8.477849E+04, 8.563874E+04,
8.650398E+04, 8.737419E+04, 8.824940E+04, 8.912959E+04, 9.001479E+04, 9.090498E+04,
9.180018E+04, 9.270040E+04, 9.360563E+04, 9.451588E+04, 9.543115E+04, 9.635146E+04,
9.727679E+04, 9.820716E+04, 9.914257E+04, 1.000830E+05, 1.010285E+05, 1.019791E+05,
1.029347E+05, 1.038953E+05, 1.048611E+05, 1.058318E+05, 1.068077E+05, 1.077886E+05,
1.087746E+05, 1.097656E+05, 1.107617E+05, 1.117630E+05, 1.127692E+05, 1.137806E+05,
1.147971E+05, 1.158186E+05, 1.168452E+05, 1.178769E+05, 1.189137E+05, 1.199556E+05,
1.210026E+05, 1.220546E+05, 1.231118E+05, 1.241741E+05, 1.252414E+05, 1.263139E+05,
1.273914E+05, 1.284741E+05, 1.295618E+05, 1.306547E+05, 1.317526E+05, 1.328556E+05,
1.339638E+05, 1.350770E+05, 1.361953E+05, 1.373188E+05, 1.384473E+05, 1.395809E+05,
1.407197E+05, 1.418635E+05, 1.430124E+05, 1.441664E+05, 1.453255E+05, 1.464897E+05,
1.476590E+05, 1.488334E+05, 1.500129E+05, 1.511974E+05, 1.523871E+05, 1.535818E+05,
1.547816E+05, 1.559865E+05, 1.571964E+05, 1.584115E+05, 1.596316E+05, 1.608568E+05,
1.620871E+05, 1.633224E+05, 1.645628E+05, 1.658083E+05, 1.670588E+05, 1.683144E+05,
1.695750E+05, 1.708407E+05, 1.721114E+05, 1.733872E+05, 1.746680E+05, 1.759539E+05,
1.772448E+05, 1.785407E+05, 1.798417E+05, 1.811477E+05, 1.824587E+05, 1.837748E+05,
1.850958E+05, 1.864219E+05, 1.877530E+05, 1.890890E+05, 1.904301E+05, 1.917762E+05,
1.931273E+05, 1.944833E+05, 1.958443E+05, 1.972104E+05, 1.985814E+05, 1.999573E+05,
2.013382E+05, 2.027241E+05, 2.041150E+05, 2.055107E+05, 2.069115E+05, 2.083171E+05,
2.097278E+05, 2.111433E+05, 2.125638E+05, 2.139891E+05, 2.154194E+05, 2.168546E+05,
2.182947E+05, 2.197397E+05, 2.211896E+05, 2.226444E+05, 2.241041E+05, 2.255686E+05,
2.270380E+05, 2.285122E+05, 2.299914E+05, 2.314753E+05, 2.329641E+05, 2.344578E+05,
2.359562E+05, 2.374595E+05, 2.389676E+05, 2.404806E+05, 2.419983E+05, 2.435208E+05,
2.450481E+05, 2.465802E+05, 2.481171E+05, 2.496587E+05, 2.512051E+05, 2.527563E+05,
2.543122E+05, 2.558729E+05, 2.574382E+05, 2.590083E+05, 2.605832E+05, 2.621627E+05,
2.637469E+05, 2.653359E+05, 2.669295E+05, 2.685278E+05, 2.701308E+05, 2.717384E+05,
2.733507E+05, 2.749677E+05, 2.765893E+05, 2.782155E+05, 2.798463E+05, 2.814818E+05,
2.831219E+05, 2.847665E+05, 2.864158E+05, 2.880697E+05, 2.897281E+05, 2.913911E+05,
2.930586E+05, 2.947307E+05, 2.964074E+05, 2.980885E+05, 2.997742E+05, 3.014644E+05,
3.031591E+05, 3.048584E+05, 3.065620E+05, 3.082702E+05, 3.099829E+05, 3.117000E+05,
3.134215E+05, 3.151475E+05, 3.168780E+05, 3.186128E+05, 3.203521E+05, 3.220958E+05,
3.238439E+05, 3.255963E+05, 3.273532E+05, 3.291144E+05, 3.308799E+05, 3.326499E+05,
3.344241E+05, 3.362027E+05, 3.379856E+05, 3.397728E+05,
])
# ---------------------- M = 7, I = 6 ---------------------------
M = 7
I = 6
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[5]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
7.197400E+01, 5.586187E+02, 1.111229E+03, 1.665410E+03, 2.220057E+03, 2.774938E+03,
3.329973E+03, 3.885127E+03, 4.440388E+03, 4.995759E+03, 5.551272E+03, 6.106999E+03,
6.663066E+03, 7.219666E+03, 7.777057E+03, 8.335560E+03, 8.895550E+03, 9.457446E+03,
1.002170E+04, 1.058877E+04, 1.115914E+04, 1.173327E+04, 1.231163E+04, 1.289467E+04,
1.348281E+04, 1.407645E+04, 1.467598E+04, 1.528176E+04, 1.589412E+04, 1.651337E+04,
1.713979E+04, 1.777365E+04, 1.841520E+04, 1.906465E+04, 1.972223E+04, 2.038811E+04,
2.106247E+04, 2.174549E+04, 2.243730E+04, 2.313805E+04, 2.384786E+04, 2.456684E+04,
2.529512E+04, 2.603279E+04, 2.677994E+04, 2.753666E+04, 2.830303E+04, 2.907913E+04,
2.986503E+04, 3.066080E+04, 3.146649E+04, 3.228216E+04, 3.310788E+04, 3.394370E+04,
3.478965E+04, 3.564581E+04, 3.651220E+04, 3.738887E+04, 3.827587E+04, 3.917323E+04,
4.008100E+04, 4.099922E+04, 4.192792E+04, 4.286713E+04, 4.381690E+04, 4.477727E+04,
4.574825E+04, 4.672990E+04, 4.772225E+04, 4.872532E+04, 4.973916E+04, 5.076379E+04,
5.179925E+04, 5.284558E+04, 5.390280E+04, 5.497096E+04, 5.605008E+04, 5.714021E+04,
5.824136E+04, 5.935359E+04, 6.047692E+04, 6.161139E+04, 6.275703E+04, 6.391387E+04,
6.508197E+04, 6.626134E+04, 6.745202E+04, 6.865406E+04, 6.986749E+04, 7.109234E+04,
7.232865E+04, 7.357646E+04, 7.483581E+04, 7.610672E+04, 7.738925E+04, 7.868343E+04,
7.998930E+04, 8.130689E+04, 8.263624E+04, 8.397740E+04, 8.533040E+04, 8.669528E+04,
8.807207E+04, 8.946082E+04, 9.086157E+04, 9.227436E+04, 9.369922E+04, 9.513619E+04,
9.658532E+04, 9.804664E+04, 9.952019E+04, 1.010060E+05, 1.025041E+05, 1.040146E+05,
1.055375E+05, 1.070728E+05, 1.086206E+05, 1.101808E+05, 1.117536E+05, 1.133390E+05,
1.149371E+05, 1.165477E+05, 1.181711E+05, 1.198072E+05, 1.214561E+05, 1.231178E+05,
1.247924E+05, 1.264798E+05, 1.281802E+05, 1.298935E+05, 1.316198E+05, 1.333592E+05,
1.351116E+05, 1.368771E+05, 1.386558E+05, 1.404477E+05, 1.422528E+05, 1.440711E+05,
1.459027E+05, 1.477476E+05, 1.496059E+05, 1.514775E+05, 1.533626E+05, 1.552611E+05,
1.571732E+05, 1.590987E+05, 1.610378E+05, 1.629905E+05, 1.649567E+05, 1.669367E+05,
1.689303E+05, 1.709376E+05, 1.729586E+05, 1.749935E+05, 1.770421E+05, 1.791045E+05,
1.811808E+05, 1.832710E+05, 1.853751E+05, 1.874931E+05, 1.896251E+05, 1.917711E+05,
1.939311E+05, 1.961052E+05, 1.982934E+05, 2.004956E+05, 2.027120E+05, 2.049425E+05,
2.071872E+05, 2.094461E+05, 2.117192E+05, 2.140066E+05, 2.163082E+05, 2.186241E+05,
2.209544E+05, 2.232989E+05, 2.256579E+05, 2.280312E+05, 2.304189E+05, 2.328211E+05,
2.352376E+05, 2.376687E+05, 2.401142E+05, 2.425742E+05, 2.450488E+05, 2.475379E+05,
2.500415E+05, 2.525597E+05, 2.550925E+05, 2.576400E+05, 2.602020E+05, 2.627787E+05,
2.653700E+05, 2.679761E+05, 2.705968E+05, 2.732322E+05, 2.758823E+05, 2.785471E+05,
2.812268E+05, 2.839211E+05, 2.866302E+05, 2.893542E+05, 2.920929E+05, 2.948464E+05,
2.976148E+05, 3.003980E+05, 3.031960E+05, 3.060089E+05, 3.088366E+05, 3.116792E+05,
3.145368E+05, 3.174092E+05, 3.202965E+05, 3.231987E+05, 3.261158E+05, 3.290479E+05,
3.319949E+05, 3.349569E+05, 3.379338E+05, 3.409257E+05, 3.439325E+05, 3.469543E+05,
3.499911E+05, 3.530429E+05, 3.561096E+05, 3.591914E+05, 3.622881E+05, 3.653999E+05,
3.685267E+05, 3.716684E+05, 3.748252E+05, 3.779970E+05, 3.811839E+05, 3.843857E+05,
3.876026E+05, 3.908345E+05, 3.940814E+05, 3.973434E+05, 4.006204E+05, 4.039124E+05,
4.072195E+05, 4.105416E+05, 4.138787E+05, 4.172308E+05, 4.205980E+05, 4.239803E+05,
4.273775E+05, 4.307898E+05, 4.342171E+05, 4.376594E+05, 4.411168E+05, 4.445892E+05,
4.480765E+05, 4.515790E+05, 4.550964E+05, 4.586288E+05, 4.621762E+05, 4.657386E+05,
4.693160E+05, 4.729085E+05, 4.765158E+05, 4.801382E+05, 4.837756E+05, 4.874279E+05,
4.910951E+05, 4.947774E+05, 4.984745E+05, 5.021866E+05, 5.059137E+05, 5.096557E+05,
5.134126E+05, 5.171844E+05, 5.209711E+05, 5.247727E+05, 5.285892E+05, 5.324206E+05,
5.362668E+05, 5.401279E+05, 5.440038E+05, 5.478946E+05, 5.518002E+05, 5.557207E+05,
5.596559E+05, 5.636060E+05, 5.675708E+05, 5.715504E+05, 5.755448E+05, 5.795539E+05,
5.835777E+05, 5.876163E+05, 5.916696E+05, 5.957376E+05, 5.998203E+05, 6.039177E+05,
6.080298E+05, 6.121564E+05, 6.162978E+05, 6.204537E+05, 6.246243E+05, 6.288094E+05,
6.330091E+05, 6.372234E+05, 6.414523E+05, 6.456957E+05, 6.499535E+05, 6.542259E+05,
6.585128E+05, 6.628142E+05, 6.671300E+05, 6.714602E+05, 6.758049E+05, 6.801639E+05,
6.845374E+05, 6.889252E+05, 6.933274E+05, 6.977439E+05, 7.021747E+05, 7.066198E+05,
7.110792E+05, 7.155528E+05, 7.200407E+05, 7.245429E+05, 7.290592E+05, 7.335897E+05,
7.381343E+05, 7.426932E+05, 7.472661E+05, 7.518531E+05, 7.564543E+05, 7.610694E+05,
7.656987E+05, 7.703419E+05, 7.749992E+05, 7.796704E+05, 7.843556E+05, 7.890547E+05,
7.937678E+05, 7.984947E+05, 8.032355E+05, 8.079901E+05, 8.127586E+05, 8.175409E+05,
8.223370E+05, 8.271468E+05, 8.319703E+05, 8.368076E+05, 8.416586E+05, 8.465232E+05,
8.514015E+05, 8.562933E+05, 8.611988E+05, 8.661178E+05, 8.710504E+05, 8.759965E+05,
8.809561E+05, 8.859292E+05, 8.909157E+05, 8.959156E+05, 9.009290E+05, 9.059557E+05,
9.109957E+05, 9.160491E+05, 9.211157E+05, 9.261956E+05, 9.312888E+05, 9.363952E+05,
9.415147E+05, 9.466474E+05, 9.517933E+05, 9.569522E+05, 9.621243E+05, 9.673094E+05,
9.725075E+05, 9.777186E+05, 9.829427E+05, 9.881797E+05,
])
# ---------------------- M = 8, I = 1 ---------------------------
M = 8
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.187882E+01, 5.438944E+01, 1.046755E+02, 1.605493E+02, 2.237848E+02, 2.936074E+02,
3.693790E+02, 4.493987E+02, 5.325091E+02, 6.180282E+02, 7.054511E+02, 7.943992E+02,
8.845954E+02, 9.758218E+02, 1.067921E+03, 1.160750E+03, 1.254256E+03, 1.348352E+03,
1.443007E+03, 1.538179E+03, 1.633904E+03, 1.730134E+03, 1.826913E+03, 1.924263E+03,
2.022194E+03, 2.120743E+03, 2.219926E+03, 2.319814E+03, 2.420412E+03, 2.521749E+03,
2.623906E+03, 2.726868E+03, 2.830696E+03, 2.935398E+03, 3.041038E+03, 3.147623E+03,
3.255221E+03, 3.363806E+03, 3.473413E+03, 3.584110E+03, 3.695900E+03, 3.808781E+03,
3.922787E+03, 4.037949E+03, 4.154301E+03, 4.271797E+03, 4.390503E+03, 4.510451E+03,
4.631630E+03, 4.754023E+03, 4.877700E+03, 5.002642E+03, 5.128876E+03, 5.256379E+03,
5.385224E+03, 5.515334E+03, 5.646783E+03, 5.779540E+03, 5.913678E+03, 6.049113E+03,
6.185916E+03, 6.324108E+03, 6.463595E+03, 6.604508E+03, 6.746748E+03, 6.890451E+03,
7.035454E+03, 7.181892E+03, 7.329656E+03, 7.478888E+03, 7.629473E+03, 7.781490E+03,
7.934955E+03, 8.089742E+03, 8.246004E+03, 8.403681E+03, 8.562787E+03, 8.723261E+03,
8.885184E+03, 9.048571E+03, 9.213358E+03, 9.379626E+03, 9.547237E+03, 9.716351E+03,
9.886903E+03, 1.005890E+04, 1.023227E+04, 1.040718E+04, 1.058348E+04, 1.076127E+04,
1.094045E+04, 1.112113E+04, 1.130323E+04, 1.148684E+04, 1.167188E+04, 1.185836E+04,
1.204629E+04, 1.223566E+04, 1.242649E+04, 1.261887E+04, 1.281263E+04, 1.300787E+04,
1.320458E+04, 1.340278E+04, 1.360238E+04, 1.380347E+04, 1.400606E+04, 1.421006E+04,
1.441557E+04, 1.462260E+04, 1.483105E+04, 1.504093E+04, 1.525234E+04, 1.546529E+04,
1.567956E+04, 1.589539E+04, 1.611265E+04, 1.633148E+04, 1.655176E+04, 1.677349E+04,
1.699667E+04, 1.722133E+04, 1.744744E+04, 1.767514E+04, 1.790419E+04, 1.813483E+04,
1.836696E+04, 1.860044E+04, 1.883553E+04, 1.907199E+04, 1.931006E+04, 1.954950E+04,
1.979055E+04, 2.003299E+04, 2.027692E+04, 2.052237E+04, 2.076931E+04, 2.101764E+04,
2.126762E+04, 2.151898E+04, 2.177186E+04, 2.202612E+04, 2.228205E+04, 2.253936E+04,
2.279821E+04, 2.305845E+04, 2.332023E+04, 2.358354E+04, 2.384840E+04, 2.411465E+04,
2.438245E+04, 2.465165E+04, 2.492240E+04, 2.519456E+04, 2.546842E+04, 2.574354E+04,
2.602037E+04, 2.629846E+04, 2.657826E+04, 2.685948E+04, 2.714211E+04, 2.742631E+04,
2.771194E+04, 2.799913E+04, 2.828791E+04, 2.857810E+04, 2.886972E+04, 2.916292E+04,
2.945755E+04, 2.975376E+04, 3.005141E+04, 3.035064E+04, 3.065130E+04, 3.095340E+04,
3.125708E+04, 3.156221E+04,
])
# ---------------------- M = 8, I = 2 ---------------------------
M = 8
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
7.941320E+00, 3.745917E+01, 7.221608E+01, 1.108280E+02, 1.545250E+02, 2.027736E+02,
2.552148E+02, 3.105134E+02, 3.679482E+02, 4.270480E+02, 4.874637E+02, 5.489392E+02,
6.112725E+02, 6.743180E+02, 7.379665E+02, 8.021363E+02, 8.667588E+02, 9.318076E+02,
9.972455E+02, 1.063065E+03, 1.129260E+03, 1.195844E+03, 1.262803E+03, 1.330177E+03,
1.397975E+03, 1.466208E+03, 1.534920E+03, 1.604131E+03, 1.673861E+03, 1.744132E+03,
1.814968E+03, 1.886392E+03, 1.958449E+03, 2.031145E+03, 2.104487E+03, 2.178540E+03,
2.253291E+03, 2.328765E+03, 2.404963E+03, 2.481933E+03, 2.559679E+03, 2.638222E+03,
2.717563E+03, 2.797724E+03, 2.878728E+03, 2.960569E+03, 3.043241E+03, 3.126792E+03,
3.211244E+03, 3.296556E+03, 3.382746E+03, 3.469862E+03, 3.557861E+03, 3.646792E+03,
3.736671E+03, 3.827446E+03, 3.919168E+03, 4.011816E+03, 4.105405E+03, 4.199946E+03,
4.295455E+03, 4.391908E+03, 4.489355E+03, 4.587730E+03, 4.687084E+03, 4.787429E+03,
4.888735E+03, 4.991012E+03, 5.094314E+03, 5.198564E+03, 5.303815E+03, 5.410032E+03,
5.517270E+03, 5.625490E+03, 5.734750E+03, 5.844962E+03, 5.956183E+03, 6.068418E+03,
6.181628E+03, 6.295921E+03, 6.411150E+03, 6.527425E+03, 6.644701E+03, 6.762984E+03,
6.882281E+03, 7.002598E+03, 7.123940E+03, 7.246257E+03, 7.369611E+03, 7.494009E+03,
7.619456E+03, 7.745839E+03, 7.873343E+03, 8.001789E+03, 8.131307E+03, 8.261840E+03,
8.393389E+03, 8.525963E+03, 8.659562E+03, 8.794193E+03, 8.929860E+03, 9.066566E+03,
9.204314E+03, 9.343044E+03, 9.482892E+03, 9.623727E+03, 9.765553E+03, 9.908441E+03,
1.005239E+04, 1.019735E+04, 1.034337E+04, 1.049040E+04, 1.063844E+04, 1.078756E+04,
1.093769E+04, 1.108883E+04, 1.124107E+04, 1.139433E+04, 1.154862E+04, 1.170400E+04,
1.186034E+04, 1.201778E+04, 1.217626E+04, 1.233577E+04, 1.249631E+04, 1.265790E+04,
1.282053E+04, 1.298420E+04, 1.314892E+04, 1.331460E+04, 1.348141E+04, 1.364928E+04,
1.381820E+04, 1.398809E+04, 1.415904E+04, 1.433114E+04, 1.450421E+04, 1.467825E+04,
1.485345E+04, 1.502963E+04, 1.520687E+04, 1.538519E+04, 1.556458E+04, 1.574495E+04,
1.592640E+04, 1.610883E+04, 1.629244E+04, 1.647703E+04, 1.666261E+04, 1.684928E+04,
1.703703E+04, 1.722587E+04, 1.741570E+04, 1.760653E+04, 1.779854E+04, 1.799145E+04,
1.818556E+04, 1.838056E+04, 1.857677E+04, 1.877397E+04, 1.897217E+04, 1.917148E+04,
1.937178E+04, 1.957319E+04, 1.977572E+04, 1.997924E+04, 2.018376E+04, 2.038940E+04,
2.059605E+04, 2.080370E+04, 2.101258E+04, 2.122235E+04, 2.143324E+04, 2.164514E+04,
2.185817E+04, 2.207220E+04,
])
# ---------------------- M = 8, I = 3 ---------------------------
M = 8
I = 3
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.189184E+01, 5.706583E+01, 1.101111E+02, 1.690336E+02, 2.357137E+02, 3.093392E+02,
3.894382E+02, 4.738273E+02, 5.614765E+02, 6.516668E+02, 7.438655E+02, 8.376816E+02,
9.328071E+02, 1.029019E+03, 1.126152E+03, 1.224093E+03, 1.322726E+03, 1.422012E+03,
1.521894E+03, 1.622376E+03, 1.723436E+03, 1.825094E+03, 1.927347E+03, 2.030239E+03,
2.133784E+03, 2.238044E+03, 2.343022E+03, 2.448769E+03, 2.555318E+03, 2.662726E+03,
2.771034E+03, 2.880252E+03, 2.990447E+03, 3.101631E+03, 3.213843E+03, 3.327092E+03,
3.441482E+03, 3.556955E+03, 3.673617E+03, 3.791438E+03, 3.910456E+03, 4.030706E+03,
4.152188E+03, 4.274977E+03, 4.399026E+03, 4.524369E+03, 4.651037E+03, 4.779063E+03,
4.908435E+03, 5.039134E+03, 5.171234E+03, 5.304767E+03, 5.439664E+03, 5.575945E+03,
5.713690E+03, 5.852870E+03, 5.993512E+03, 6.135528E+03, 6.279050E+03, 6.424044E+03,
6.570531E+03, 6.718415E+03, 6.867831E+03, 7.018739E+03, 7.171158E+03, 7.325106E+03,
7.480473E+03, 7.637402E+03, 7.795844E+03, 7.955817E+03, 8.117334E+03, 8.280340E+03,
8.444852E+03, 8.610949E+03, 8.778504E+03, 8.947672E+03, 9.118396E+03, 9.290609E+03,
9.464401E+03, 9.639785E+03, 9.816691E+03, 9.995128E+03, 1.017511E+04, 1.035664E+04,
1.053982E+04, 1.072449E+04, 1.091073E+04, 1.109857E+04, 1.128800E+04, 1.147896E+04,
1.167153E+04, 1.186564E+04, 1.206128E+04, 1.225857E+04, 1.245741E+04, 1.265782E+04,
1.285979E+04, 1.306344E+04, 1.326857E+04, 1.347539E+04, 1.368371E+04, 1.389363E+04,
1.410515E+04, 1.431819E+04, 1.453295E+04, 1.474923E+04, 1.496703E+04, 1.518647E+04,
1.540755E+04, 1.563017E+04, 1.585444E+04, 1.608026E+04, 1.630775E+04, 1.653679E+04,
1.676739E+04, 1.699955E+04, 1.723340E+04, 1.746882E+04, 1.770581E+04, 1.794450E+04,
1.818465E+04, 1.842652E+04, 1.866997E+04, 1.891501E+04, 1.916153E+04, 1.940978E+04,
1.965962E+04, 1.991107E+04, 2.016414E+04, 2.041882E+04, 2.067511E+04, 2.093290E+04,
2.119244E+04, 2.145347E+04, 2.171627E+04, 2.198056E+04, 2.224635E+04, 2.251392E+04,
2.278313E+04, 2.305384E+04, 2.332620E+04, 2.360007E+04, 2.387573E+04, 2.415291E+04,
2.443174E+04, 2.471209E+04, 2.499410E+04, 2.527778E+04, 2.556313E+04, 2.584999E+04,
2.613838E+04, 2.642845E+04, 2.672019E+04, 2.701361E+04, 2.730857E+04, 2.760506E+04,
2.790322E+04, 2.820309E+04, 2.850449E+04, 2.880758E+04, 2.911222E+04, 2.941855E+04,
2.972642E+04, 3.003600E+04, 3.034711E+04, 3.065978E+04, 3.097415E+04, 3.129023E+04,
3.160787E+04, 3.192705E+04, 3.224796E+04, 3.257041E+04, 3.289459E+04, 3.322032E+04,
3.354777E+04, 3.387661E+04,
])
# ---------------------- M = 9, I = 1 ---------------------------
M = 9
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.454300E+00, 1.022319E+02, 2.882009E+02, 5.289590E+02, 8.141879E+02, 1.138246E+03,
1.498364E+03, 1.893680E+03, 2.324649E+03, 2.792534E+03, 3.299086E+03, 3.846299E+03,
4.436548E+03, 5.072346E+03, 5.756332E+03, 6.491539E+03, 7.281118E+03, 8.128285E+03,
9.036571E+03, 1.000965E+04, 1.105141E+04, 1.216570E+04, 1.335683E+04, 1.462901E+04,
1.598660E+04, 1.743436E+04, 1.897692E+04, 2.061904E+04, 2.236584E+04, 2.422246E+04,
2.619414E+04, 2.828645E+04, 3.050468E+04, 3.285482E+04, 3.534284E+04, 3.797440E+04,
4.075593E+04, 4.369377E+04, 4.679420E+04, 5.006431E+04, 5.351042E+04, 5.713968E+04,
6.095944E+04, 6.497664E+04, 6.919892E+04, 7.363414E+04, 7.828963E+04, 8.317352E+04,
8.829416E+04, 9.365966E+04, 9.927870E+04, 1.051597E+05, 1.113113E+05, 1.177434E+05,
1.244642E+05, 1.314837E+05, 1.388114E+05, 1.464570E+05, 1.544299E+05, 1.627414E+05,
1.714005E+05, 1.804188E+05, 1.898065E+05, 1.995743E+05, 2.097337E+05, 2.202957E+05,
2.312719E+05, 2.426739E+05, 2.545138E+05, 2.668032E+05, 2.795549E+05, 2.927811E+05,
3.064944E+05, 3.207079E+05, 3.354346E+05, 3.506881E+05, 3.664818E+05, 3.828290E+05,
3.997436E+05, 4.172406E+05, 4.353337E+05, 4.540374E+05, 4.733667E+05, 4.933375E+05,
5.139636E+05, 5.352603E+05, 5.572451E+05, 5.799322E+05, 6.033387E+05, 6.274802E+05,
6.523742E+05, 6.780366E+05, 7.044845E+05, 7.317353E+05, 7.598073E+05, 7.887176E+05,
8.184840E+05, 8.491238E+05, 8.806575E+05, 9.131028E+05, 9.464777E+05, 9.808023E+05,
1.016096E+06, 1.052379E+06, 1.089669E+06, 1.127988E+06, 1.167357E+06, 1.207794E+06,
1.249321E+06, 1.291961E+06, 1.335732E+06, 1.380659E+06, 1.426761E+06, 1.474061E+06,
1.522583E+06, 1.572347E+06, 1.623377E+06, 1.675696E+06, 1.729329E+06, 1.784297E+06,
1.840624E+06, 1.898336E+06, 1.957457E+06, 2.018012E+06, 2.080025E+06, 2.143521E+06,
2.208524E+06, 2.275064E+06, 2.343163E+06, 2.412851E+06, 2.484152E+06, 2.557091E+06,
2.631700E+06, 2.708003E+06, 2.786027E+06, 2.865802E+06, 2.947356E+06, 3.030718E+06,
3.115914E+06, 3.202976E+06, 3.291932E+06, 3.382810E+06, 3.475643E+06, 3.570459E+06,
3.667290E+06, 3.766164E+06, 3.867117E+06, 3.970174E+06, 4.075372E+06, 4.182739E+06,
4.292312E+06, 4.404117E+06, 4.518192E+06, 4.634568E+06, 4.753277E+06, 4.874358E+06,
4.997837E+06, 5.123754E+06, 5.252144E+06, 5.383038E+06, 5.516472E+06, 5.652484E+06,
5.791109E+06, 5.932381E+06, 6.076339E+06, 6.223016E+06, 6.372454E+06, 6.524687E+06,
6.679752E+06, 6.837689E+06, 6.998536E+06, 7.162331E+06, 7.329112E+06, 7.498922E+06,
7.671795E+06, 7.847772E+06,
])
# ---------------------- M = 9, I = 2 ---------------------------
M = 9
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.314830E+00, 1.027046E+02, 2.895361E+02, 5.313995E+02, 8.179047E+02, 1.143446E+03,
1.505213E+03, 1.902320E+03, 2.335259E+03, 2.805284E+03, 3.314118E+03, 3.863828E+03,
4.456728E+03, 5.095327E+03, 5.782419E+03, 6.520963E+03, 7.314000E+03, 8.164932E+03,
9.077250E+03, 1.005464E+04, 1.110102E+04, 1.222023E+04, 1.341664E+04, 1.469432E+04,
1.605802E+04, 1.751204E+04, 1.906127E+04, 2.071061E+04, 2.246511E+04, 2.432974E+04,
2.631009E+04, 2.841143E+04, 3.063941E+04, 3.299970E+04, 3.549845E+04, 3.814136E+04,
4.093511E+04, 4.388563E+04, 4.699944E+04, 5.028368E+04, 5.374467E+04, 5.738960E+04,
6.122559E+04, 6.526014E+04, 6.950067E+04, 7.395475E+04, 7.863036E+04, 8.353538E+04,
8.867784E+04, 9.406655E+04, 9.970955E+04, 1.056157E+05, 1.117939E+05, 1.182531E+05,
1.250031E+05, 1.320525E+05, 1.394112E+05, 1.470895E+05, 1.550966E+05, 1.634432E+05,
1.721394E+05, 1.811964E+05, 1.906239E+05, 2.004330E+05, 2.106356E+05, 2.212424E+05,
2.322657E+05, 2.437162E+05, 2.556059E+05, 2.679482E+05, 2.807536E+05, 2.940361E+05,
3.078079E+05, 3.220813E+05, 3.368710E+05, 3.521889E+05, 3.680495E+05, 3.844658E+05,
4.014520E+05, 4.190232E+05, 4.371929E+05, 4.559761E+05, 4.753874E+05, 4.954424E+05,
5.161552E+05, 5.375425E+05, 5.596200E+05, 5.824029E+05, 6.059083E+05, 6.301519E+05,
6.551502E+05, 6.809212E+05, 7.074811E+05, 7.348473E+05, 7.630374E+05, 7.920695E+05,
8.219613E+05, 8.527311E+05, 8.843978E+05, 9.169790E+05, 9.504949E+05, 9.849644E+05,
1.020406E+06, 1.056841E+06, 1.094290E+06, 1.132770E+06, 1.172304E+06, 1.212911E+06,
1.254614E+06, 1.297432E+06, 1.341388E+06, 1.386503E+06, 1.432799E+06, 1.480299E+06,
1.529023E+06, 1.578997E+06, 1.630242E+06, 1.682781E+06, 1.736639E+06, 1.791837E+06,
1.848402E+06, 1.906357E+06, 1.965726E+06, 2.026534E+06, 2.088807E+06, 2.152569E+06,
2.217847E+06, 2.284665E+06, 2.353051E+06, 2.423030E+06, 2.494629E+06, 2.567875E+06,
2.642796E+06, 2.719418E+06, 2.797771E+06, 2.877882E+06, 2.959776E+06, 3.043487E+06,
3.129040E+06, 3.216467E+06, 3.305794E+06, 3.397055E+06, 3.490276E+06, 3.585490E+06,
3.682726E+06, 3.782016E+06, 3.883388E+06, 3.986878E+06, 4.092517E+06, 4.200333E+06,
4.310363E+06, 4.422637E+06, 4.537190E+06, 4.654052E+06, 4.773258E+06, 4.894845E+06,
5.018842E+06, 5.145286E+06, 5.274211E+06, 5.405652E+06, 5.539647E+06, 5.676225E+06,
5.815431E+06, 5.957293E+06, 6.101851E+06, 6.249144E+06, 6.399204E+06, 6.552073E+06,
6.707787E+06, 6.866385E+06, 7.027904E+06, 7.192382E+06, 7.359862E+06, 7.530380E+06,
7.703973E+06, 7.880689E+06,
])
# ---------------------- M = 10, I = 1 ---------------------------
M = 10
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
3.067800E+00, 2.332671E+02, 6.566540E+02, 1.204608E+03, 1.853462E+03, 2.589584E+03,
3.403999E+03, 4.290795E+03, 5.246348E+03, 6.268939E+03, 7.358458E+03, 8.515917E+03,
9.743304E+03, 1.104335E+04, 1.241945E+04, 1.387519E+04, 1.541486E+04, 1.704306E+04,
1.876472E+04, 2.058489E+04, 2.250873E+04, 2.454224E+04, 2.669106E+04, 2.896127E+04,
3.135884E+04, 3.389046E+04, 3.656272E+04, 3.938207E+04, 4.235539E+04, 4.549001E+04,
4.879273E+04, 5.227161E+04, 5.593368E+04, 5.978687E+04, 6.383887E+04, 6.809790E+04,
7.257236E+04, 7.727028E+04, 8.220086E+04, 8.737241E+04, 9.279450E+04, 9.847505E+04,
1.044246E+05, 1.106525E+05, 1.171684E+05, 1.239821E+05, 1.311035E+05, 1.385434E+05,
1.463120E+05, 1.544204E+05, 1.628794E+05, 1.717004E+05, 1.808944E+05, 1.904724E+05,
2.004476E+05, 2.108313E+05, 2.216352E+05, 2.328730E+05, 2.445559E+05, 2.566978E+05,
2.693113E+05, 2.824111E+05, 2.960088E+05, 3.101196E+05, 3.247566E+05, 3.399344E+05,
3.556676E+05, 3.719701E+05, 3.888595E+05, 4.063475E+05, 4.244521E+05, 4.431882E+05,
4.625704E+05, 4.826163E+05, 5.033429E+05, 5.247666E+05, 5.469033E+05, 5.697703E+05,
5.933851E+05, 6.177656E+05, 6.429297E+05, 6.688959E+05, 6.956828E+05, 7.233073E+05,
7.517906E+05, 7.811517E+05, 8.114085E+05, 8.425809E+05, 8.746909E+05, 9.077571E+05,
9.417998E+05, 9.768401E+05, 1.012899E+06, 1.050001E+06, 1.088162E+06, 1.127407E+06,
1.167759E+06, 1.209239E+06, 1.251871E+06, 1.295677E+06, 1.340680E+06, 1.386904E+06,
1.434373E+06, 1.483110E+06, 1.533142E+06, 1.584490E+06, 1.637182E+06, 1.691244E+06,
1.746699E+06, 1.803572E+06, 1.861893E+06, 1.921685E+06, 1.982978E+06, 2.045797E+06,
2.110167E+06, 2.176121E+06, 2.243683E+06, 2.312882E+06, 2.383749E+06, 2.456307E+06,
2.530594E+06, 2.606631E+06, 2.684450E+06, 2.764086E+06, 2.845564E+06, 2.928916E+06,
3.014177E+06, 3.101370E+06, 3.190535E+06, 3.281700E+06, 3.374897E+06, 3.470160E+06,
3.567520E+06, 3.667012E+06, 3.768671E+06, 3.872526E+06, 3.978618E+06, 4.086977E+06,
4.197639E+06, 4.310638E+06, 4.426011E+06, 4.543793E+06, 4.664020E+06, 4.786729E+06,
4.911957E+06, 5.039746E+06, 5.170124E+06, 5.303134E+06, 5.438818E+06, 5.577205E+06,
5.718344E+06, 5.862272E+06, 6.009019E+06, 6.158638E+06, 6.311162E+06, 6.466636E+06,
6.625096E+06, 6.786586E+06, 6.951149E+06, 7.118822E+06, 7.289654E+06, 7.463683E+06,
7.640954E+06, 7.821510E+06, 8.005394E+06, 8.192658E+06, 8.383334E+06, 8.577472E+06,
8.775125E+06, 8.976324E+06, 9.181131E+06, 9.389576E+06, 9.601719E+06, 9.817605E+06,
1.003727E+07, 1.026078E+07,
])
# ---------------------- M = 11, I = 1 ---------------------------
M = 11
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[1]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
3.834090E+00, 3.544986E+01, 8.933083E+01, 1.601234E+02, 2.441067E+02, 3.392668E+02,
4.443978E+02, 5.586602E+02, 6.814457E+02, 8.123287E+02, 9.510416E+02, 1.097456E+03,
1.251567E+03, 1.413475E+03, 1.583372E+03, 1.761529E+03, 1.948287E+03, 2.144046E+03,
2.349255E+03, 2.564410E+03, 2.790043E+03, 3.026722E+03, 3.275047E+03, 3.535644E+03,
3.809167E+03, 4.096297E+03, 4.397737E+03, 4.714220E+03, 5.046499E+03, 5.395358E+03,
5.761605E+03, 6.146077E+03, 6.549640E+03, 6.973192E+03, 7.417660E+03, 7.884006E+03,
8.373224E+03, 8.886347E+03, 9.424443E+03, 9.988617E+03, 1.058002E+04, 1.119983E+04,
1.184929E+04, 1.252967E+04, 1.324228E+04, 1.398851E+04, 1.476975E+04, 1.558747E+04,
1.644319E+04, 1.733848E+04, 1.827495E+04, 1.925429E+04, 2.027822E+04, 2.134854E+04,
2.246709E+04, 2.363579E+04, 2.485661E+04, 2.613158E+04, 2.746282E+04, 2.885248E+04,
3.030281E+04, 3.181610E+04, 3.339475E+04, 3.504120E+04, 3.675797E+04, 3.854766E+04,
4.041296E+04, 4.235662E+04, 4.438148E+04, 4.649045E+04, 4.868654E+04, 5.097282E+04,
5.335248E+04, 5.582878E+04, 5.840505E+04, 6.108474E+04, 6.387137E+04, 6.676857E+04,
6.978004E+04, 7.290961E+04, 7.616117E+04, 7.953873E+04, 8.304638E+04, 8.668833E+04,
9.046886E+04, 9.439239E+04, 9.846341E+04, 1.026865E+05, 1.070664E+05, 1.116079E+05,
1.163160E+05, 1.211955E+05, 1.262517E+05, 1.314898E+05, 1.369150E+05, 1.425329E+05,
1.483489E+05, 1.543688E+05, 1.605982E+05, 1.670429E+05, 1.737090E+05, 1.806025E+05,
1.877295E+05, 1.950963E+05, 2.027093E+05, 2.105748E+05, 2.186995E+05, 2.270900E+05,
2.357531E+05, 2.446956E+05, 2.539243E+05, 2.634465E+05, 2.732691E+05, 2.833994E+05,
2.938447E+05, 3.046123E+05, 3.157098E+05, 3.271446E+05, 3.389245E+05, 3.510570E+05,
3.635501E+05, 3.764116E+05, 3.896493E+05, 4.032714E+05, 4.172859E+05, 4.317008E+05,
4.465246E+05, 4.617653E+05, 4.774313E+05, 4.935310E+05, 5.100729E+05, 5.270654E+05,
5.445171E+05, 5.624366E+05, 5.808324E+05, 5.997132E+05, 6.190879E+05, 6.389651E+05,
6.593535E+05, 6.802621E+05, 7.016996E+05, 7.236749E+05, 7.461970E+05, 7.692747E+05,
7.929169E+05, 8.171327E+05, 8.419309E+05, 8.673206E+05, 8.933106E+05, 9.199101E+05,
9.471280E+05, 9.749732E+05, 1.003455E+06, 1.032582E+06, 1.062363E+06, 1.092807E+06,
1.123924E+06, 1.155722E+06, 1.188210E+06, 1.221397E+06, 1.255292E+06, 1.289903E+06,
1.325240E+06, 1.361311E+06, 1.398126E+06, 1.435692E+06, 1.474019E+06, 1.513114E+06,
1.552988E+06, 1.593648E+06, 1.635103E+06, 1.677361E+06, 1.720431E+06, 1.764321E+06,
1.809040E+06, 1.854596E+06, 1.900996E+06, 1.948250E+06, 1.996365E+06, 2.045349E+06,
2.095211E+06, 2.145958E+06, 2.197599E+06, 2.250140E+06, 2.303590E+06, 2.357956E+06,
2.413246E+06, 2.469467E+06, 2.526627E+06, 2.584734E+06, 2.643794E+06, 2.703815E+06,
2.764803E+06, 2.826766E+06, 2.889712E+06, 2.953646E+06, 3.018575E+06, 3.084507E+06,
3.151448E+06, 3.219404E+06, 3.288382E+06, 3.358388E+06, 3.429429E+06, 3.501511E+06,
3.574640E+06, 3.648822E+06, 3.724062E+06, 3.800367E+06, 3.877743E+06, 3.956195E+06,
4.035729E+06, 4.116350E+06, 4.198063E+06, 4.280875E+06, 4.364789E+06, 4.449812E+06,
4.535948E+06, 4.623202E+06, 4.711579E+06, 4.801083E+06, 4.891720E+06, 4.983494E+06,
5.076409E+06, 5.170470E+06, 5.265680E+06, 5.362045E+06, 5.459568E+06, 5.558252E+06,
5.658103E+06, 5.759124E+06, 5.861317E+06, 5.964688E+06, 6.069240E+06, 6.174975E+06,
6.281898E+06, 6.390011E+06, 6.499317E+06, 6.609820E+06, 6.721523E+06, 6.834428E+06,
6.948538E+06, 7.063856E+06, 7.180384E+06, 7.298125E+06, 7.417081E+06, 7.537255E+06,
7.658648E+06, 7.781264E+06, 7.905103E+06, 8.030168E+06, 8.156460E+06, 8.283982E+06,
8.412735E+06, 8.542721E+06, 8.673941E+06, 8.806397E+06, 8.940089E+06, 9.075020E+06,
9.211190E+06, 9.348600E+06, 9.487252E+06, 9.627147E+06, 9.768284E+06, 9.910666E+06,
1.005429E+07, 1.019916E+07, 1.034528E+07, 1.049264E+07, 1.064125E+07, 1.079111E+07,
1.094222E+07, 1.109457E+07, 1.124817E+07, 1.140301E+07, 1.155911E+07, 1.171645E+07,
1.187503E+07, 1.203487E+07, 1.219595E+07, 1.235827E+07, 1.252184E+07, 1.268666E+07,
1.285272E+07, 1.302002E+07, 1.318857E+07, 1.335836E+07, 1.352938E+07, 1.370165E+07,
1.387516E+07, 1.404990E+07, 1.422589E+07, 1.440310E+07, 1.458155E+07, 1.476124E+07,
1.494215E+07, 1.512430E+07, 1.530767E+07, 1.549227E+07, 1.567809E+07, 1.586514E+07,
1.605341E+07,
])
# ---------------------- M = 11, I = 2 ---------------------------
M = 11
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[1]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
2.689360E+00, 2.371743E+01, 5.973090E+01, 1.070476E+02, 1.631783E+02, 2.267778E+02,
2.970405E+02, 3.734061E+02, 4.554693E+02, 5.429478E+02, 6.356653E+02, 7.335395E+02,
8.365694E+02, 9.448250E+02, 1.058437E+03, 1.177588E+03, 1.302505E+03, 1.433451E+03,
1.570721E+03, 1.714634E+03, 1.865531E+03, 2.023770E+03, 2.189723E+03, 2.363775E+03,
2.546320E+03, 2.737759E+03, 2.938503E+03, 3.148970E+03, 3.369585E+03, 3.600778E+03,
3.842989E+03, 4.096664E+03, 4.362256E+03, 4.640230E+03, 4.931055E+03, 5.235214E+03,
5.553197E+03, 5.885507E+03, 6.232655E+03, 6.595167E+03, 6.973580E+03, 7.368444E+03,
7.780322E+03, 8.209792E+03, 8.657444E+03, 9.123885E+03, 9.609736E+03, 1.011563E+04,
1.064223E+04, 1.119020E+04, 1.176022E+04, 1.235300E+04, 1.296927E+04, 1.360974E+04,
1.427520E+04, 1.496640E+04, 1.568415E+04, 1.642926E+04, 1.720255E+04, 1.800489E+04,
1.883715E+04, 1.970021E+04, 2.059500E+04, 2.152244E+04, 2.248350E+04, 2.347915E+04,
2.451040E+04, 2.557826E+04, 2.668378E+04, 2.782802E+04, 2.901208E+04, 3.023707E+04,
3.150413E+04, 3.281442E+04, 3.416912E+04, 3.556945E+04, 3.701663E+04, 3.851194E+04,
4.005664E+04, 4.165205E+04, 4.329951E+04, 4.500037E+04, 4.675603E+04, 4.856789E+04,
5.043739E+04, 5.236600E+04, 5.435520E+04, 5.640652E+04, 5.852151E+04, 6.070172E+04,
6.294876E+04, 6.526426E+04, 6.764987E+04, 7.010727E+04, 7.263815E+04, 7.524427E+04,
7.792737E+04, 8.068925E+04, 8.353171E+04, 8.645661E+04, 8.946581E+04, 9.256120E+04,
9.574471E+04, 9.901829E+04, 1.023839E+05, 1.058436E+05, 1.093993E+05, 1.130531E+05,
1.168072E+05, 1.206635E+05, 1.246243E+05, 1.286917E+05, 1.328678E+05, 1.371549E+05,
1.415551E+05, 1.460708E+05, 1.507042E+05, 1.554576E+05, 1.603333E+05, 1.653337E+05,
1.704610E+05, 1.757178E+05, 1.811063E+05, 1.866290E+05, 1.922883E+05, 1.980868E+05,
2.040268E+05, 2.101108E+05, 2.163414E+05, 2.227210E+05, 2.292523E+05, 2.359378E+05,
2.427799E+05, 2.497814E+05, 2.569448E+05, 2.642727E+05, 2.717677E+05, 2.794324E+05,
2.872695E+05, 2.952817E+05, 3.034715E+05, 3.118417E+05, 3.203948E+05, 3.291337E+05,
3.380608E+05, 3.471791E+05, 3.564910E+05, 3.659994E+05, 3.757069E+05, 3.856162E+05,
3.957300E+05, 4.060510E+05, 4.165819E+05, 4.273253E+05, 4.382841E+05, 4.494608E+05,
4.608582E+05, 4.724789E+05, 4.843256E+05, 4.964010E+05, 5.087078E+05, 5.212486E+05,
5.340260E+05, 5.470427E+05, 5.603014E+05, 5.738046E+05, 5.875549E+05, 6.015551E+05,
6.158075E+05, 6.303150E+05, 6.450799E+05, 6.601048E+05, 6.753923E+05, 6.909449E+05,
7.067651E+05, 7.228554E+05, 7.392182E+05, 7.558561E+05, 7.727714E+05, 7.899665E+05,
8.074439E+05, 8.252060E+05, 8.432551E+05, 8.615934E+05, 8.802235E+05, 8.991475E+05,
9.183677E+05, 9.378864E+05, 9.577058E+05, 9.778282E+05, 9.982556E+05, 1.018990E+06,
1.040034E+06, 1.061390E+06, 1.083059E+06, 1.105044E+06, 1.127346E+06, 1.149969E+06,
1.172913E+06, 1.196180E+06, 1.219773E+06, 1.243694E+06, 1.267944E+06, 1.292525E+06,
1.317439E+06, 1.342688E+06, 1.368273E+06, 1.394197E+06, 1.420461E+06, 1.447066E+06,
1.474015E+06, 1.501309E+06, 1.528949E+06, 1.556938E+06, 1.585276E+06, 1.613965E+06,
1.643007E+06, 1.672403E+06, 1.702154E+06, 1.732262E+06, 1.762729E+06, 1.793555E+06,
1.824742E+06, 1.856291E+06, 1.888203E+06, 1.920480E+06, 1.953123E+06, 1.986132E+06,
2.019510E+06, 2.053256E+06, 2.087373E+06, 2.121861E+06, 2.156721E+06, 2.191954E+06,
2.227562E+06, 2.263544E+06, 2.299902E+06, 2.336637E+06, 2.373749E+06, 2.411240E+06,
2.449110E+06, 2.487359E+06, 2.525989E+06, 2.565001E+06, 2.604394E+06, 2.644169E+06,
2.684328E+06, 2.724871E+06, 2.765797E+06, 2.807108E+06, 2.848805E+06, 2.890886E+06,
2.933354E+06, 2.976209E+06, 3.019450E+06, 3.063078E+06, 3.107094E+06, 3.151498E+06,
3.196289E+06, 3.241469E+06, 3.287037E+06, 3.332994E+06, 3.379340E+06, 3.426074E+06,
3.473198E+06, 3.520711E+06, 3.568613E+06, 3.616905E+06, 3.665585E+06, 3.714655E+06,
3.764114E+06, 3.813962E+06, 3.864200E+06, 3.914826E+06, 3.965841E+06, 4.017245E+06,
4.069037E+06, 4.121218E+06, 4.173787E+06, 4.226743E+06, 4.280088E+06, 4.333819E+06,
4.387938E+06, 4.442443E+06, 4.497335E+06, 4.552613E+06, 4.608276E+06, 4.664325E+06,
4.720758E+06, 4.777576E+06, 4.834778E+06, 4.892363E+06, 4.950330E+06, 5.008681E+06,
5.067413E+06, 5.126526E+06, 5.186021E+06, 5.245895E+06, 5.306149E+06, 5.366782E+06,
5.427793E+06,
])
# ---------------------- M = 12, I = 1 ---------------------------
M = 12
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
3.573524E+01, 2.896824E+03, 8.174539E+03, 1.500830E+04, 2.310835E+04, 3.233901E+04,
4.267884E+04, 5.420733E+04, 6.708747E+04, 8.154826E+04, 9.787188E+04, 1.163871E+05,
1.374678E+05, 1.615349E+05, 1.890610E+05, 2.205764E+05, 2.566761E+05, 2.980283E+05,
3.453830E+05, 3.995815E+05, 4.615675E+05, 5.323980E+05, 6.132560E+05, 7.054636E+05,
8.104973E+05, 9.300032E+05, 1.065814E+06, 1.219969E+06, 1.394732E+06, 1.592616E+06,
1.816403E+06, 2.069175E+06, 2.354336E+06, 2.675644E+06, 3.037246E+06, 3.443706E+06,
3.900048E+06, 4.411790E+06, 4.984997E+06, 5.626315E+06, 6.343026E+06, 7.143101E+06,
8.035255E+06, 9.029012E+06, 1.013476E+07, 1.136382E+07, 1.272855E+07, 1.424236E+07,
1.591986E+07, 1.777692E+07, 1.983076E+07, 2.210007E+07, 2.460508E+07, 2.736772E+07,
3.041171E+07, 3.376270E+07, 3.744839E+07, 4.149872E+07, 4.594593E+07, 5.082484E+07,
5.617291E+07, 6.203049E+07, 6.844098E+07, 7.545107E+07, 8.311086E+07, 9.147421E+07,
1.005989E+08, 1.105468E+08, 1.213843E+08, 1.331825E+08, 1.460176E+08, 1.599709E+08,
1.751294E+08, 1.915862E+08, 2.094406E+08, 2.287987E+08, 2.497734E+08, 2.724856E+08,
2.970635E+08, 3.236443E+08, 3.523735E+08, 3.834064E+08, 4.169078E+08, 4.530531E+08,
4.920286E+08, 5.340321E+08, 5.792739E+08, 6.279767E+08, 6.803770E+08, 7.367252E+08,
7.972871E+08, 8.623440E+08, 9.321937E+08, 1.007151E+09, 1.087551E+09, 1.173744E+09,
1.266104E+09, 1.365025E+09, 1.470923E+09, 1.584237E+09, 1.705430E+09, 1.834993E+09,
1.973439E+09, 2.121315E+09, 2.279192E+09, 2.447675E+09, 2.627399E+09, 2.819035E+09,
3.023286E+09, 3.240894E+09, 3.472638E+09, 3.719338E+09, 3.981857E+09, 4.261099E+09,
4.558015E+09, 4.873603E+09, 5.208913E+09, 5.565043E+09, 5.943148E+09, 6.344438E+09,
6.770183E+09, 7.221711E+09, 7.700418E+09, 8.207763E+09, 8.745275E+09, 9.314557E+09,
9.917283E+09, 1.055521E+10, 1.123017E+10, 1.194408E+10, 1.269895E+10, 1.349688E+10,
1.434006E+10, 1.523078E+10, 1.617143E+10, 1.716451E+10, 1.821263E+10, 1.931852E+10,
2.048501E+10, 2.171506E+10, 2.301179E+10, 2.437840E+10, 2.581826E+10, 2.733487E+10,
2.893189E+10, 3.061313E+10, 3.238254E+10, 3.424425E+10, 3.620258E+10, 3.826198E+10,
4.042712E+10, 4.270285E+10, 4.509420E+10, 4.760643E+10, 5.024498E+10, 5.301553E+10,
5.592397E+10, 5.897644E+10, 6.217929E+10, 6.553915E+10, 6.906289E+10, 7.275761E+10,
7.663079E+10, 8.069007E+10, 8.494343E+10, 8.939920E+10, 9.406595E+10, 9.895261E+10,
1.040684E+11, 1.094230E+11, 1.150262E+11, 1.208886E+11, 1.270205E+11, 1.334333E+11,
1.401384E+11, 1.471476E+11,
])
# ---------------------- M = 12, I = 2 ---------------------------
M = 12
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
2.382603E+01, 1.931437E+03, 5.450319E+03, 1.000669E+04, 1.540736E+04, 2.156205E+04,
2.845720E+04, 3.614739E+04, 4.474377E+04, 5.440219E+04, 6.531440E+04, 7.770355E+04,
9.182310E+04, 1.079584E+05, 1.264298E+05, 1.475967E+05, 1.718627E+05, 1.996812E+05,
2.315613E+05, 2.680746E+05, 3.098624E+05, 3.576437E+05, 4.122233E+05, 4.745014E+05,
5.454834E+05, 6.262912E+05, 7.181744E+05, 8.225232E+05, 9.408828E+05, 1.074968E+06,
1.226680E+06, 1.398121E+06, 1.591620E+06, 1.809745E+06, 2.055330E+06, 2.331499E+06,
2.641689E+06, 2.989677E+06, 3.379614E+06, 3.816053E+06, 4.303982E+06, 4.848864E+06,
5.456671E+06, 6.133931E+06, 6.887769E+06, 7.725955E+06, 8.656957E+06, 9.689991E+06,
1.083508E+07, 1.210311E+07, 1.350592E+07, 1.505634E+07, 1.676827E+07, 1.865678E+07,
2.073817E+07, 2.303008E+07, 2.555154E+07, 2.832314E+07, 3.136707E+07, 3.470727E+07,
3.836952E+07, 4.238158E+07, 4.677333E+07, 5.157688E+07, 5.682677E+07, 6.256005E+07,
6.881650E+07, 7.563878E+07, 8.307263E+07, 9.116702E+07, 9.997437E+07, 1.095508E+08,
1.199563E+08, 1.312550E+08, 1.435154E+08, 1.568106E+08, 1.712185E+08, 1.868225E+08,
2.037110E+08, 2.219786E+08, 2.417258E+08, 2.630596E+08, 2.860939E+08, 3.109496E+08,
3.377555E+08, 3.666480E+08, 3.977722E+08, 4.312820E+08, 4.673407E+08, 5.061213E+08,
5.478072E+08, 5.925928E+08, 6.406837E+08, 6.922978E+08, 7.476655E+08, 8.070303E+08,
8.706501E+08, 9.387968E+08, 1.011758E+09, 1.089838E+09, 1.173356E+09, 1.262651E+09,
1.358080E+09, 1.460018E+09, 1.568862E+09, 1.685029E+09, 1.808960E+09, 1.941117E+09,
2.081987E+09, 2.232084E+09, 2.391947E+09, 2.562142E+09, 2.743268E+09, 2.935948E+09,
3.140842E+09, 3.358640E+09, 3.590069E+09, 3.835888E+09, 4.096897E+09, 4.373935E+09,
4.667879E+09, 4.979648E+09, 5.310211E+09, 5.660576E+09, 6.031804E+09, 6.425001E+09,
6.841331E+09, 7.282006E+09, 7.748297E+09, 8.241535E+09, 8.763108E+09, 9.314471E+09,
9.897139E+09, 1.051270E+10, 1.116282E+10, 1.184921E+10, 1.257370E+10, 1.333817E+10,
1.414458E+10, 1.499499E+10, 1.589154E+10, 1.683648E+10, 1.783212E+10, 1.888090E+10,
1.998535E+10, 2.114810E+10, 2.237192E+10, 2.365965E+10, 2.501428E+10, 2.643891E+10,
2.793676E+10, 2.951121E+10, 3.116574E+10, 3.290399E+10, 3.472976E+10, 3.664695E+10,
3.865967E+10, 4.077217E+10, 4.298886E+10, 4.531432E+10, 4.775333E+10, 5.031083E+10,
5.299197E+10, 5.580208E+10, 5.874669E+10, 6.183156E+10, 6.506265E+10, 6.844617E+10,
7.198850E+10, 7.569632E+10, 7.957652E+10, 8.363629E+10, 8.788300E+10, 9.232438E+10,
9.696836E+10, 1.018233E+11,
])
# ---------------------- M = 13, I = 1 ---------------------------
M = 13
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[3]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.538496E+01, 1.602673E+01, 1.728778E+01, 2.005293E+01, 2.374009E+01, 2.800797E+01,
3.266623E+01, 3.759941E+01, 4.273326E+01, 4.801797E+01, 5.341894E+01, 5.891142E+01,
6.447729E+01, 7.010295E+01, 7.577806E+01, 8.149456E+01, 8.724613E+01, 9.302771E+01,
9.883525E+01, 1.046654E+02, 1.105156E+02, 1.163834E+02, 1.222671E+02, 1.281652E+02,
1.340764E+02, 1.399998E+02, 1.459345E+02, 1.518800E+02, 1.578359E+02, 1.638019E+02,
1.697777E+02, 1.757636E+02, 1.817594E+02, 1.877655E+02, 1.937821E+02, 1.998096E+02,
2.058484E+02, 2.118992E+02, 2.179624E+02, 2.240388E+02, 2.301290E+02, 2.362337E+02,
2.423537E+02, 2.484898E+02, 2.546428E+02, 2.608134E+02, 2.670027E+02, 2.732114E+02,
2.794403E+02, 2.856903E+02, 2.919624E+02, 2.982572E+02, 3.045757E+02, 3.109188E+02,
3.172871E+02, 3.236817E+02, 3.301032E+02, 3.365525E+02, 3.430303E+02, 3.495374E+02,
3.560745E+02, 3.626425E+02, 3.692419E+02, 3.758735E+02, 3.825380E+02, 3.892360E+02,
3.959682E+02, 4.027353E+02, 4.095377E+02, 4.163762E+02, 4.232513E+02, 4.301635E+02,
4.371135E+02, 4.441017E+02, 4.511287E+02, 4.581950E+02, 4.653011E+02, 4.724475E+02,
4.796345E+02, 4.868628E+02, 4.941326E+02, 5.014445E+02, 5.087989E+02, 5.161961E+02,
5.236366E+02, 5.311207E+02, 5.386489E+02, 5.462214E+02, 5.538387E+02, 5.615011E+02,
5.692089E+02, 5.769624E+02, 5.847620E+02, 5.926080E+02, 6.005006E+02, 6.084403E+02,
6.164272E+02, 6.244617E+02, 6.325440E+02, 6.406744E+02, 6.488532E+02, 6.570806E+02,
6.653569E+02, 6.736823E+02, 6.820571E+02, 6.904815E+02, 6.989557E+02, 7.074801E+02,
7.160547E+02, 7.246798E+02, 7.333557E+02, 7.420826E+02, 7.508606E+02, 7.596900E+02,
7.685709E+02, 7.775036E+02, 7.864883E+02, 7.955252E+02, 8.046144E+02, 8.137561E+02,
8.229506E+02, 8.321980E+02, 8.414985E+02, 8.508522E+02, 8.602594E+02, 8.697202E+02,
8.792348E+02, 8.888034E+02, 8.984261E+02, 9.081031E+02, 9.178345E+02, 9.276206E+02,
9.374615E+02, 9.473573E+02, 9.573083E+02, 9.673144E+02, 9.773761E+02, 9.874933E+02,
9.976662E+02, 1.007895E+03, 1.018180E+03, 1.028521E+03, 1.038918E+03, 1.049372E+03,
1.059883E+03, 1.070450E+03, 1.081075E+03, 1.091756E+03, 1.102495E+03, 1.113291E+03,
1.124144E+03, 1.135055E+03, 1.146024E+03, 1.157051E+03, 1.168136E+03, 1.179280E+03,
1.190482E+03, 1.201742E+03, 1.213061E+03, 1.224439E+03, 1.235875E+03, 1.247371E+03,
1.258926E+03, 1.270541E+03, 1.282215E+03, 1.293948E+03, 1.305742E+03, 1.317595E+03,
1.329509E+03, 1.341483E+03, 1.353517E+03, 1.365612E+03, 1.377767E+03, 1.389983E+03,
1.402260E+03, 1.414599E+03, 1.426998E+03, 1.439459E+03, 1.451981E+03, 1.464566E+03,
1.477211E+03, 1.489919E+03, 1.502689E+03, 1.515521E+03, 1.528416E+03, 1.541373E+03,
1.554393E+03, 1.567476E+03, 1.580621E+03, 1.593830E+03, 1.607102E+03, 1.620437E+03,
1.633836E+03, 1.647299E+03, 1.660825E+03, 1.674416E+03, 1.688070E+03, 1.701789E+03,
1.715573E+03, 1.729421E+03, 1.743334E+03, 1.757311E+03, 1.771354E+03, 1.785462E+03,
1.799635E+03, 1.813874E+03, 1.828179E+03, 1.842549E+03, 1.856985E+03, 1.871488E+03,
1.886056E+03, 1.900691E+03, 1.915393E+03, 1.930162E+03, 1.944997E+03, 1.959899E+03,
1.974869E+03, 1.989906E+03, 2.005010E+03, 2.020183E+03, 2.035423E+03, 2.050731E+03,
2.066107E+03, 2.081552E+03, 2.097065E+03, 2.112647E+03, 2.128297E+03, 2.144017E+03,
2.159805E+03, 2.175663E+03, 2.191591E+03, 2.207588E+03, 2.223654E+03, 2.239791E+03,
2.255998E+03, 2.272275E+03, 2.288623E+03, 2.305041E+03, 2.321530E+03, 2.338090E+03,
2.354721E+03, 2.371423E+03, 2.388197E+03, 2.405042E+03, 2.421959E+03, 2.438948E+03,
2.456009E+03, 2.473143E+03, 2.490349E+03, 2.507627E+03, 2.524978E+03, 2.542402E+03,
2.559899E+03, 2.577470E+03, 2.595114E+03, 2.612831E+03, 2.630622E+03, 2.648488E+03,
2.666427E+03, 2.684441E+03, 2.702529E+03, 2.720691E+03, 2.738929E+03, 2.757241E+03,
2.775629E+03, 2.794091E+03, 2.812630E+03, 2.831243E+03, 2.849933E+03, 2.868698E+03,
2.887540E+03, 2.906458E+03, 2.925453E+03, 2.944524E+03, 2.963671E+03, 2.982896E+03,
3.002198E+03, 3.021577E+03, 3.041034E+03, 3.060568E+03, 3.080180E+03, 3.099870E+03,
3.119638E+03, 3.139484E+03, 3.159409E+03, 3.179412E+03, 3.199494E+03, 3.219655E+03,
3.239895E+03, 3.260214E+03, 3.280613E+03, 3.301091E+03, 3.321649E+03, 3.342287E+03,
3.363005E+03, 3.383803E+03, 3.404681E+03, 3.425640E+03, 3.446680E+03, 3.467800E+03,
3.489001E+03, 3.510284E+03, 3.531647E+03, 3.553093E+03, 3.574619E+03, 3.596228E+03,
3.617918E+03, 3.639691E+03, 3.661545E+03, 3.683482E+03, 3.705502E+03, 3.727604E+03,
3.749788E+03, 3.772056E+03, 3.794407E+03, 3.816841E+03, 3.839358E+03, 3.861959E+03,
3.884644E+03, 3.907412E+03, 3.930264E+03, 3.953201E+03, 3.976221E+03, 3.999326E+03,
4.022515E+03, 4.045789E+03, 4.069147E+03, 4.092591E+03, 4.116119E+03, 4.139733E+03,
4.163432E+03, 4.187216E+03, 4.211086E+03, 4.235041E+03, 4.259082E+03, 4.283209E+03,
4.307422E+03, 4.331721E+03, 4.356107E+03, 4.380578E+03, 4.405137E+03, 4.429781E+03,
4.454513E+03, 4.479331E+03, 4.504237E+03, 4.529229E+03, 4.554309E+03, 4.579476E+03,
4.604730E+03, 4.630072E+03, 4.655502E+03, 4.681019E+03, 4.706624E+03, 4.732317E+03,
4.758098E+03, 4.783967E+03, 4.809924E+03, 4.835970E+03, 4.862104E+03, 4.888326E+03,
4.914637E+03, 4.941037E+03, 4.967526E+03, 4.994104E+03, 5.020770E+03, 5.047526E+03,
5.074371E+03, 5.101305E+03, 5.128328E+03, 5.155441E+03, 5.182643E+03, 5.209935E+03,
5.237316E+03, 5.264787E+03, 5.292348E+03, 5.319999E+03, 5.347739E+03, 5.375570E+03,
5.403491E+03, 5.431502E+03, 5.459603E+03, 5.487794E+03, 5.516076E+03, 5.544448E+03,
5.572910E+03, 5.601464E+03, 5.630107E+03, 5.658842E+03, 5.687667E+03, 5.716582E+03,
5.745589E+03, 5.774686E+03, 5.803874E+03, 5.833154E+03, 5.862524E+03, 5.891985E+03,
5.921537E+03, 5.951181E+03, 5.980916E+03, 6.010741E+03, 6.040658E+03, 6.070667E+03,
6.100766E+03, 6.130957E+03, 6.161240E+03, 6.191614E+03, 6.222079E+03, 6.252635E+03,
6.283284E+03, 6.314023E+03, 6.344855E+03, 6.375777E+03, 6.406792E+03, 6.437898E+03,
6.469095E+03, 6.500385E+03, 6.531765E+03, 6.563238E+03, 6.594802E+03, 6.626458E+03,
6.658206E+03, 6.690045E+03, 6.721976E+03, 6.753999E+03, 6.786113E+03, 6.818319E+03,
6.850617E+03, 6.883007E+03, 6.915488E+03, 6.948061E+03, 6.980726E+03, 7.013482E+03,
7.046330E+03, 7.079270E+03, 7.112302E+03, 7.145425E+03, 7.178640E+03, 7.211946E+03,
7.245344E+03, 7.278834E+03, 7.312415E+03, 7.346088E+03, 7.379852E+03, 7.413708E+03,
7.447655E+03, 7.481694E+03, 7.515824E+03, 7.550045E+03, 7.584358E+03, 7.618763E+03,
7.653258E+03,
])
# ---------------------- M = 13, I = 2 ---------------------------
M = 13
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.597389E+01, 1.605971E+01, 1.732986E+01, 2.012387E+01, 2.384501E+01, 2.814934E+01,
3.284552E+01, 3.781754E+01, 4.299087E+01, 4.831550E+01, 5.375673E+01, 5.928972E+01,
6.489630E+01, 7.056284E+01, 7.627897E+01, 8.203662E+01, 8.783033E+01, 9.365333E+01,
9.950433E+01, 1.053783E+02, 1.112735E+02, 1.171880E+02, 1.231211E+02, 1.290729E+02,
1.350413E+02, 1.410255E+02, 1.470291E+02, 1.530492E+02, 1.590887E+02, 1.651462E+02,
1.712253E+02, 1.773248E+02, 1.834470E+02, 1.895945E+02, 1.957642E+02, 2.019629E+02,
2.081875E+02, 2.144411E+02, 2.207268E+02, 2.270436E+02, 2.333928E+02, 2.397777E+02,
2.461972E+02, 2.526550E+02, 2.591499E+02, 2.656857E+02, 2.722610E+02, 2.788770E+02,
2.855378E+02, 2.922419E+02, 2.989905E+02, 3.057878E+02, 3.126291E+02, 3.195186E+02,
3.264576E+02, 3.334441E+02, 3.404823E+02, 3.475737E+02, 3.547126E+02, 3.619069E+02,
3.691508E+02, 3.764523E+02, 3.838055E+02, 3.912149E+02, 3.986779E+02, 4.061993E+02,
4.137762E+02, 4.214096E+02, 4.290963E+02, 4.368453E+02, 4.446536E+02, 4.525179E+02,
4.604431E+02, 4.684258E+02, 4.764670E+02, 4.845716E+02, 4.927317E+02, 5.009570E+02,
5.092437E+02, 5.175879E+02, 5.259950E+02, 5.344656E+02, 5.429958E+02, 5.515910E+02,
5.602469E+02, 5.689641E+02, 5.777484E+02, 5.865952E+02, 5.955001E+02, 6.044738E+02,
6.135065E+02, 6.226095E+02, 6.317725E+02, 6.410014E+02, 6.502913E+02, 6.596482E+02,
6.690726E+02, 6.785594E+02, 6.881090E+02, 6.977277E+02, 7.074100E+02, 7.171565E+02,
7.269735E+02, 7.368494E+02, 7.467968E+02, 7.568101E+02, 7.668831E+02, 7.770291E+02,
7.872422E+02, 7.975161E+02, 8.078577E+02, 8.182674E+02, 8.287457E+02, 8.392861E+02,
8.498957E+02, 8.605749E+02, 8.713171E+02, 8.821296E+02, 8.930058E+02, 9.039529E+02,
9.149714E+02, 9.260471E+02, 9.372022E+02, 9.484149E+02, 9.597003E+02, 9.710512E+02,
9.824755E+02, 9.939582E+02, 1.005515E+03, 1.017146E+03, 1.028836E+03, 1.040601E+03,
1.052425E+03, 1.064325E+03, 1.076292E+03, 1.088328E+03, 1.100432E+03, 1.112604E+03,
1.124845E+03, 1.137155E+03, 1.149542E+03, 1.161991E+03, 1.174509E+03, 1.187097E+03,
1.199746E+03, 1.212474E+03, 1.225273E+03, 1.238133E+03, 1.251065E+03, 1.264076E+03,
1.277150E+03, 1.290286E+03, 1.303503E+03, 1.316783E+03, 1.330135E+03, 1.343559E+03,
1.357046E+03, 1.370616E+03, 1.384249E+03, 1.397945E+03, 1.411724E+03, 1.425567E+03,
1.439474E+03, 1.453465E+03, 1.467520E+03, 1.481639E+03, 1.495832E+03, 1.510100E+03,
1.524443E+03, 1.538850E+03, 1.553323E+03, 1.567870E+03, 1.582494E+03, 1.597182E+03,
1.611947E+03, 1.626776E+03,
])
# ---------------------- M = 13, I = 3 ---------------------------
M = 13
I = 3
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
2.398602E+01, 2.529396E+01, 3.190248E+01, 4.103232E+01, 5.180328E+01, 6.382165E+01,
7.678389E+01, 9.046186E+01, 1.046887E+02, 1.193429E+02, 1.343350E+02, 1.495984E+02,
1.650827E+02, 1.807493E+02, 1.965682E+02, 2.125155E+02, 2.285724E+02, 2.447236E+02,
2.609566E+02, 2.772613E+02, 2.936290E+02, 3.100527E+02, 3.265296E+02, 3.430483E+02,
3.596111E+02, 3.762111E+02, 3.928490E+02, 4.095183E+02, 4.262208E+02, 4.429551E+02,
4.597196E+02, 4.765132E+02, 4.933399E+02, 5.101943E+02, 5.270811E+02, 5.439947E+02,
5.609459E+02, 5.779297E+02, 5.949463E+02, 6.120082E+02, 6.290982E+02, 6.462353E+02,
6.634146E+02, 6.806371E+02, 6.979040E+02, 7.152234E+02, 7.325899E+02, 7.500121E+02,
7.674917E+02, 7.850226E+02, 8.026143E+02, 8.202684E+02, 8.379869E+02, 8.557801E+02,
8.736330E+02, 8.915558E+02, 9.095506E+02, 9.276191E+02, 9.457727E+02, 9.639948E+02,
9.823062E+02, 1.000699E+03, 1.019167E+03, 1.037730E+03, 1.056380E+03, 1.075110E+03,
1.093943E+03, 1.112858E+03, 1.131880E+03, 1.150989E+03, 1.170197E+03, 1.189507E+03,
1.208908E+03, 1.228415E+03, 1.248030E+03, 1.267742E+03, 1.287565E+03, 1.307488E+03,
1.327527E+03, 1.347670E+03, 1.367918E+03, 1.388287E+03, 1.408751E+03, 1.429339E+03,
1.450040E+03, 1.470854E+03, 1.491784E+03, 1.512831E+03, 1.533996E+03, 1.555282E+03,
1.576689E+03, 1.598204E+03, 1.619844E+03, 1.641594E+03, 1.663488E+03, 1.685495E+03,
1.707617E+03, 1.729870E+03, 1.752241E+03, 1.774729E+03, 1.797354E+03, 1.820116E+03,
1.842984E+03, 1.865991E+03, 1.889123E+03, 1.912380E+03, 1.935764E+03, 1.959275E+03,
1.982915E+03, 2.006703E+03, 2.030604E+03, 2.054636E+03, 2.078802E+03, 2.103083E+03,
2.127519E+03, 2.152071E+03, 2.176760E+03, 2.201588E+03, 2.226555E+03, 2.251642E+03,
2.276871E+03, 2.302222E+03, 2.327716E+03, 2.353353E+03, 2.379116E+03, 2.405003E+03,
2.431038E+03, 2.457199E+03, 2.483508E+03, 2.509946E+03, 2.536534E+03, 2.563252E+03,
2.590101E+03, 2.617102E+03, 2.644213E+03, 2.671478E+03, 2.698899E+03, 2.726432E+03,
2.754121E+03, 2.781922E+03, 2.809883E+03, 2.837979E+03, 2.866213E+03, 2.894608E+03,
2.923117E+03, 2.951765E+03, 2.980577E+03, 3.009504E+03, 3.038597E+03, 3.067806E+03,
3.097157E+03, 3.126676E+03, 3.156312E+03, 3.186092E+03, 3.216016E+03, 3.246085E+03,
3.276300E+03, 3.306660E+03, 3.337166E+03, 3.367793E+03, 3.398568E+03, 3.429490E+03,
3.460562E+03, 3.491782E+03, 3.523124E+03, 3.554617E+03, 3.586261E+03, 3.618055E+03,
3.649974E+03, 3.682073E+03, 3.714268E+03, 3.746645E+03, 3.779146E+03, 3.811803E+03,
3.844614E+03, 3.877551E+03,
])
# ---------------------- M = 14, I = 1 ---------------------------
M = 14
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[1]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
4.000000E+00, 4.626210E+00, 6.976320E+00, 9.595800E+00, 1.226348E+01, 1.494872E+01,
1.764266E+01, 2.034174E+01, 2.304424E+01, 2.574926E+01, 2.845624E+01, 3.116485E+01,
3.387487E+01, 3.658615E+01, 3.929857E+01, 4.201207E+01, 4.472657E+01, 4.744205E+01,
5.015846E+01, 5.287579E+01, 5.559401E+01, 5.831312E+01, 6.103312E+01, 6.375401E+01,
6.647583E+01, 6.919858E+01, 7.192233E+01, 7.464713E+01, 7.737305E+01, 8.010018E+01,
8.282863E+01, 8.555853E+01, 8.829002E+01, 9.102328E+01, 9.375848E+01, 9.649584E+01,
9.923557E+01, 1.019779E+02, 1.047231E+02, 1.074715E+02, 1.102232E+02, 1.129788E+02,
1.157383E+02, 1.185022E+02, 1.212707E+02, 1.240443E+02, 1.268232E+02, 1.296079E+02,
1.323986E+02, 1.351957E+02, 1.379995E+02, 1.408105E+02, 1.436289E+02, 1.464552E+02,
1.492896E+02, 1.521325E+02, 1.549843E+02, 1.578453E+02, 1.607158E+02, 1.635962E+02,
1.664868E+02, 1.693878E+02, 1.722998E+02, 1.752228E+02, 1.781573E+02, 1.811035E+02,
1.840617E+02, 1.870323E+02, 1.900154E+02, 1.930114E+02, 1.960205E+02, 1.990429E+02,
2.020790E+02, 2.051289E+02, 2.081930E+02, 2.112714E+02, 2.143644E+02, 2.174721E+02,
2.205948E+02, 2.237328E+02, 2.268862E+02, 2.300551E+02, 2.332399E+02, 2.364407E+02,
2.396577E+02, 2.428910E+02, 2.461408E+02, 2.494073E+02, 2.526907E+02, 2.559911E+02,
2.593087E+02, 2.626436E+02, 2.659960E+02, 2.693660E+02, 2.727538E+02, 2.761594E+02,
2.795831E+02, 2.830250E+02, 2.864851E+02, 2.899636E+02, 2.934607E+02, 2.969764E+02,
3.005109E+02, 3.040643E+02, 3.076367E+02, 3.112281E+02, 3.148388E+02, 3.184687E+02,
3.221181E+02, 3.257869E+02, 3.294754E+02, 3.331835E+02, 3.369115E+02, 3.406592E+02,
3.444270E+02, 3.482148E+02, 3.520228E+02, 3.558509E+02, 3.596994E+02, 3.635682E+02,
3.674576E+02, 3.713674E+02, 3.752979E+02, 3.792490E+02, 3.832209E+02, 3.872136E+02,
3.912273E+02, 3.952619E+02, 3.993176E+02, 4.033944E+02, 4.074924E+02, 4.116116E+02,
4.157521E+02, 4.199140E+02, 4.240974E+02, 4.283022E+02, 4.325286E+02, 4.367767E+02,
4.410464E+02, 4.453379E+02, 4.496512E+02, 4.539863E+02, 4.583434E+02, 4.627224E+02,
4.671235E+02, 4.715467E+02, 4.759920E+02, 4.804595E+02, 4.849493E+02, 4.894613E+02,
4.939958E+02, 4.985526E+02, 5.031320E+02, 5.077338E+02, 5.123582E+02, 5.170052E+02,
5.216749E+02, 5.263673E+02, 5.310825E+02, 5.358205E+02, 5.405813E+02, 5.453651E+02,
5.501718E+02, 5.550016E+02, 5.598544E+02, 5.647303E+02, 5.696293E+02, 5.745516E+02,
5.794971E+02, 5.844659E+02, 5.894580E+02, 5.944735E+02, 5.995124E+02, 6.045748E+02,
6.096607E+02, 6.147701E+02, 6.199032E+02, 6.250599E+02, 6.302403E+02, 6.354444E+02,
6.406723E+02, 6.459240E+02, 6.511995E+02, 6.564990E+02, 6.618224E+02, 6.671698E+02,
6.725412E+02, 6.779366E+02, 6.833562E+02, 6.887999E+02, 6.942678E+02, 6.997600E+02,
7.052764E+02, 7.108171E+02, 7.163821E+02, 7.219715E+02, 7.275854E+02, 7.332237E+02,
7.388865E+02, 7.445739E+02, 7.502858E+02, 7.560223E+02, 7.617835E+02, 7.675694E+02,
7.733800E+02, 7.792153E+02, 7.850755E+02, 7.909605E+02, 7.968704E+02, 8.028052E+02,
8.087649E+02, 8.147496E+02, 8.207593E+02, 8.267940E+02, 8.328539E+02, 8.389388E+02,
8.450490E+02, 8.511843E+02, 8.573448E+02, 8.635306E+02, 8.697416E+02, 8.759780E+02,
8.822397E+02, 8.885269E+02, 8.948394E+02, 9.011774E+02, 9.075409E+02, 9.139299E+02,
9.203444E+02, 9.267845E+02, 9.332502E+02, 9.397416E+02, 9.462586E+02, 9.528014E+02,
9.593698E+02, 9.659640E+02, 9.725841E+02, 9.792299E+02, 9.859015E+02, 9.925991E+02,
9.993225E+02, 1.006072E+03, 1.012847E+03, 1.019649E+03, 1.026476E+03, 1.033329E+03,
1.040209E+03, 1.047114E+03, 1.054046E+03, 1.061004E+03, 1.067988E+03, 1.074998E+03,
1.082034E+03, 1.089097E+03, 1.096186E+03, 1.103301E+03, 1.110443E+03, 1.117611E+03,
1.124805E+03, 1.132026E+03, 1.139273E+03, 1.146546E+03, 1.153846E+03, 1.161173E+03,
1.168525E+03, 1.175905E+03, 1.183311E+03, 1.190743E+03, 1.198203E+03, 1.205688E+03,
1.213201E+03, 1.220740E+03, 1.228306E+03, 1.235898E+03, 1.243517E+03, 1.251163E+03,
1.258835E+03, 1.266535E+03, 1.274261E+03, 1.282013E+03, 1.289793E+03, 1.297600E+03,
1.305433E+03, 1.313293E+03, 1.321180E+03, 1.329094E+03, 1.337035E+03, 1.345003E+03,
1.352997E+03, 1.361019E+03, 1.369067E+03, 1.377143E+03, 1.385245E+03, 1.393375E+03,
1.401531E+03, 1.409715E+03, 1.417925E+03, 1.426163E+03, 1.434428E+03, 1.442719E+03,
1.451038E+03,
])
# ---------------------- M = 14, I = 2 ---------------------------
M = 14
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[1]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
6.000000E+00, 1.005366E+01, 1.753287E+01, 2.515838E+01, 3.281701E+01, 4.048954E+01,
4.816994E+01, 5.585570E+01, 6.354558E+01, 7.123892E+01, 7.893531E+01, 8.663450E+01,
9.433634E+01, 1.020407E+02, 1.097475E+02, 1.174568E+02, 1.251685E+02, 1.328827E+02,
1.405995E+02, 1.483191E+02, 1.560419E+02, 1.637681E+02, 1.714984E+02, 1.792334E+02,
1.869738E+02, 1.947206E+02, 2.024749E+02, 2.102378E+02, 2.180106E+02, 2.257947E+02,
2.335917E+02, 2.414030E+02, 2.492305E+02, 2.570757E+02, 2.649405E+02, 2.728267E+02,
2.807361E+02, 2.886704E+02, 2.966316E+02, 3.046215E+02, 3.126418E+02, 3.206944E+02,
3.287810E+02, 3.369034E+02, 3.450632E+02, 3.532621E+02, 3.615017E+02, 3.697836E+02,
3.781092E+02, 3.864802E+02, 3.948978E+02, 4.033635E+02, 4.118787E+02, 4.204445E+02,
4.290623E+02, 4.377333E+02, 4.464585E+02, 4.552392E+02, 4.640764E+02, 4.729710E+02,
4.819242E+02, 4.909369E+02, 5.000099E+02, 5.091442E+02, 5.183406E+02, 5.276000E+02,
5.369230E+02, 5.463105E+02, 5.557632E+02, 5.652818E+02, 5.748670E+02, 5.845194E+02,
5.942396E+02, 6.040283E+02, 6.138860E+02, 6.238133E+02, 6.338107E+02, 6.438787E+02,
6.540179E+02, 6.642288E+02, 6.745117E+02, 6.848672E+02, 6.952957E+02, 7.057977E+02,
7.163734E+02, 7.270235E+02, 7.377481E+02, 7.485477E+02, 7.594228E+02, 7.703735E+02,
7.814004E+02, 7.925036E+02, 8.036835E+02, 8.149406E+02, 8.262749E+02, 8.376870E+02,
8.491770E+02, 8.607452E+02, 8.723920E+02, 8.841176E+02, 8.959222E+02, 9.078062E+02,
9.197698E+02, 9.318132E+02, 9.439367E+02, 9.561405E+02, 9.684249E+02, 9.807901E+02,
9.932363E+02, 1.005764E+03, 1.018373E+03, 1.031063E+03, 1.043836E+03, 1.056690E+03,
1.069627E+03, 1.082647E+03, 1.095749E+03, 1.108934E+03, 1.122202E+03, 1.135553E+03,
1.148988E+03, 1.162506E+03, 1.176109E+03, 1.189795E+03, 1.203566E+03, 1.217421E+03,
1.231360E+03, 1.245384E+03, 1.259493E+03, 1.273688E+03, 1.287967E+03, 1.302332E+03,
1.316782E+03, 1.331318E+03, 1.345940E+03, 1.360648E+03, 1.375442E+03, 1.390322E+03,
1.405289E+03, 1.420342E+03, 1.435482E+03, 1.450709E+03, 1.466023E+03, 1.481425E+03,
1.496913E+03, 1.512490E+03, 1.528153E+03, 1.543905E+03, 1.559744E+03, 1.575672E+03,
1.591687E+03, 1.607791E+03, 1.623983E+03, 1.640264E+03, 1.656633E+03, 1.673092E+03,
1.689639E+03, 1.706275E+03, 1.723000E+03, 1.739815E+03, 1.756718E+03, 1.773712E+03,
1.790795E+03, 1.807968E+03, 1.825230E+03, 1.842583E+03, 1.860025E+03, 1.877558E+03,
1.895181E+03, 1.912894E+03, 1.930698E+03, 1.948593E+03, 1.966578E+03, 1.984654E+03,
2.002820E+03, 2.021078E+03, 2.039427E+03, 2.057867E+03, 2.076398E+03, 2.095020E+03,
2.113734E+03, 2.132540E+03, 2.151437E+03, 2.170425E+03, 2.189506E+03, 2.208678E+03,
2.227942E+03, 2.247298E+03, 2.266747E+03, 2.286287E+03, 2.305920E+03, 2.325645E+03,
2.345462E+03, 2.365371E+03, 2.385374E+03, 2.405468E+03, 2.425656E+03, 2.445936E+03,
2.466309E+03, 2.486774E+03, 2.507333E+03, 2.527984E+03, 2.548729E+03, 2.569566E+03,
2.590496E+03, 2.611520E+03, 2.632637E+03, 2.653847E+03, 2.675150E+03, 2.696547E+03,
2.718037E+03, 2.739621E+03, 2.761298E+03, 2.783068E+03, 2.804932E+03, 2.826890E+03,
2.848941E+03, 2.871086E+03, 2.893324E+03, 2.915656E+03, 2.938082E+03, 2.960602E+03,
2.983215E+03, 3.005923E+03, 3.028724E+03, 3.051619E+03, 3.074608E+03, 3.097690E+03,
3.120867E+03, 3.144138E+03, 3.167502E+03, 3.190961E+03, 3.214513E+03, 3.238160E+03,
3.261900E+03, 3.285735E+03, 3.309663E+03, 3.333686E+03, 3.357802E+03, 3.382013E+03,
3.406318E+03, 3.430716E+03, 3.455209E+03, 3.479796E+03, 3.504477E+03, 3.529252E+03,
3.554120E+03, 3.579083E+03, 3.604140E+03, 3.629291E+03, 3.654536E+03, 3.679875E+03,
3.705308E+03, 3.730835E+03, 3.756455E+03, 3.782170E+03, 3.807979E+03, 3.833881E+03,
3.859877E+03, 3.885968E+03, 3.912152E+03, 3.938429E+03, 3.964801E+03, 3.991266E+03,
4.017826E+03, 4.044478E+03, 4.071225E+03, 4.098065E+03, 4.124999E+03, 4.152026E+03,
4.179147E+03, 4.206362E+03, 4.233670E+03, 4.261071E+03, 4.288566E+03, 4.316154E+03,
4.343836E+03, 4.371610E+03, 4.399478E+03, 4.427440E+03, 4.455494E+03, 4.483642E+03,
4.511883E+03, 4.540216E+03, 4.568643E+03, 4.597163E+03, 4.625776E+03, 4.654481E+03,
4.683279E+03, 4.712171E+03, 4.741154E+03, 4.770231E+03, 4.799400E+03, 4.828661E+03,
4.858015E+03, 4.887462E+03, 4.917001E+03, 4.946632E+03, 4.976356E+03, 5.006171E+03,
5.036079E+03,
])
# ---------------------- M = 15, I = 1 ---------------------------
M = 15
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[1]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
8.000000E+00, 1.379368E+01, 2.419193E+01, 3.477493E+01, 4.540023E+01, 5.604342E+01,
6.669682E+01, 7.735721E+01, 8.802303E+01, 9.869341E+01, 1.093679E+02, 1.200460E+02,
1.307278E+02, 1.414129E+02, 1.521013E+02, 1.627930E+02, 1.734880E+02, 1.841863E+02,
1.948883E+02, 2.055941E+02, 2.163042E+02, 2.270192E+02, 2.377399E+02, 2.484672E+02,
2.592022E+02, 2.699463E+02, 2.807009E+02, 2.914679E+02, 3.022490E+02, 3.130462E+02,
3.238617E+02, 3.346978E+02, 3.455568E+02, 3.564412E+02, 3.673535E+02, 3.782961E+02,
3.892717E+02, 4.002829E+02, 4.113322E+02, 4.224223E+02, 4.335555E+02, 4.447346E+02,
4.559619E+02, 4.672398E+02, 4.785708E+02, 4.899571E+02, 5.014011E+02, 5.129049E+02,
5.244706E+02, 5.361002E+02, 5.477959E+02, 5.595596E+02, 5.713930E+02, 5.832980E+02,
5.952764E+02, 6.073297E+02, 6.194597E+02, 6.316679E+02, 6.439557E+02, 6.563247E+02,
6.687761E+02, 6.813114E+02, 6.939317E+02, 7.066385E+02, 7.194327E+02, 7.323156E+02,
7.452883E+02, 7.583518E+02, 7.715072E+02, 7.847553E+02, 7.980973E+02, 8.115339E+02,
8.250660E+02, 8.386946E+02, 8.524203E+02, 8.662441E+02, 8.801667E+02, 8.941887E+02,
9.083110E+02, 9.225341E+02, 9.368588E+02, 9.512857E+02, 9.658154E+02, 9.804486E+02,
9.951858E+02, 1.010028E+03, 1.024974E+03, 1.040027E+03, 1.055186E+03, 1.070451E+03,
1.085824E+03, 1.101304E+03, 1.116893E+03, 1.132590E+03, 1.148396E+03, 1.164312E+03,
1.180338E+03, 1.196474E+03, 1.212720E+03, 1.229078E+03, 1.245547E+03, 1.262129E+03,
1.278822E+03, 1.295628E+03, 1.312548E+03, 1.329580E+03, 1.346727E+03, 1.363987E+03,
1.381362E+03, 1.398851E+03, 1.416456E+03, 1.434176E+03, 1.452011E+03, 1.469963E+03,
1.488031E+03, 1.506216E+03, 1.524517E+03, 1.542936E+03, 1.561472E+03, 1.580126E+03,
1.598899E+03, 1.617789E+03, 1.636798E+03, 1.655927E+03, 1.675174E+03, 1.694541E+03,
1.714027E+03, 1.733634E+03, 1.753361E+03, 1.773209E+03, 1.793177E+03, 1.813266E+03,
1.833477E+03, 1.853809E+03, 1.874263E+03, 1.894840E+03, 1.915538E+03, 1.936359E+03,
1.957303E+03, 1.978370E+03, 1.999561E+03, 2.020875E+03, 2.042312E+03, 2.063874E+03,
2.085560E+03, 2.107371E+03, 2.129306E+03, 2.151366E+03, 2.173551E+03, 2.195862E+03,
2.218299E+03, 2.240861E+03, 2.263549E+03, 2.286364E+03, 2.309305E+03, 2.332374E+03,
2.355569E+03, 2.378891E+03, 2.402340E+03, 2.425918E+03, 2.449623E+03, 2.473456E+03,
2.497418E+03, 2.521507E+03, 2.545726E+03, 2.570074E+03, 2.594550E+03, 2.619156E+03,
2.643891E+03, 2.668756E+03, 2.693751E+03, 2.718877E+03, 2.744132E+03, 2.769518E+03,
2.795034E+03, 2.820682E+03, 2.846460E+03, 2.872370E+03, 2.898411E+03, 2.924584E+03,
2.950888E+03, 2.977325E+03, 3.003893E+03, 3.030594E+03, 3.057428E+03, 3.084394E+03,
3.111494E+03, 3.138726E+03, 3.166091E+03, 3.193590E+03, 3.221223E+03, 3.248989E+03,
3.276889E+03, 3.304923E+03, 3.333091E+03, 3.361394E+03, 3.389831E+03, 3.418403E+03,
3.447110E+03, 3.475952E+03, 3.504929E+03, 3.534041E+03, 3.563288E+03, 3.592672E+03,
3.622191E+03, 3.651846E+03, 3.681637E+03, 3.711564E+03, 3.741627E+03, 3.771827E+03,
3.802164E+03, 3.832637E+03, 3.863247E+03, 3.893994E+03, 3.924878E+03, 3.955899E+03,
3.987057E+03, 4.018353E+03, 4.049787E+03, 4.081358E+03, 4.113067E+03, 4.144914E+03,
4.176899E+03, 4.209023E+03, 4.241284E+03, 4.273684E+03, 4.306222E+03, 4.338898E+03,
4.371714E+03, 4.404668E+03, 4.437761E+03, 4.470993E+03, 4.504364E+03, 4.537874E+03,
4.571523E+03, 4.605311E+03, 4.639239E+03, 4.673306E+03, 4.707513E+03, 4.741860E+03,
4.776346E+03, 4.810971E+03, 4.845737E+03, 4.880642E+03, 4.915688E+03, 4.950873E+03,
4.986199E+03, 5.021664E+03, 5.057270E+03, 5.093016E+03, 5.128903E+03, 5.164930E+03,
5.201097E+03, 5.237404E+03, 5.273852E+03, 5.310441E+03, 5.347170E+03, 5.384040E+03,
5.421050E+03, 5.458201E+03, 5.495493E+03, 5.532926E+03, 5.570499E+03, 5.608213E+03,
5.646068E+03, 5.684064E+03, 5.722201E+03, 5.760479E+03, 5.798897E+03, 5.837457E+03,
5.876157E+03, 5.914998E+03, 5.953981E+03, 5.993104E+03, 6.032368E+03, 6.071773E+03,
6.111319E+03, 6.151006E+03, 6.190834E+03, 6.230803E+03, 6.270913E+03, 6.311164E+03,
6.351555E+03, 6.392088E+03, 6.432761E+03, 6.473575E+03, 6.514531E+03, 6.555626E+03,
6.596863E+03, 6.638241E+03, 6.679759E+03, 6.721418E+03, 6.763217E+03, 6.805157E+03,
6.847238E+03, 6.889459E+03, 6.931821E+03, 6.974323E+03, 7.016966E+03, 7.059749E+03,
7.102672E+03,
])
# ---------------------- M = 15, I = 2 ---------------------------
M = 15
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[1]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
8.000000E+00, 1.380884E+01, 2.422364E+01, 3.482280E+01, 4.546420E+01, 5.612347E+01,
6.679294E+01, 7.746940E+01, 8.815129E+01, 9.883774E+01, 1.095283E+02, 1.202225E+02,
1.309203E+02, 1.416215E+02, 1.523260E+02, 1.630338E+02, 1.737449E+02, 1.844594E+02,
1.951774E+02, 2.058993E+02, 2.166256E+02, 2.273568E+02, 2.380936E+02, 2.488371E+02,
2.595884E+02, 2.703487E+02, 2.811197E+02, 2.919031E+02, 3.027006E+02, 3.135144E+02,
3.243465E+02, 3.351994E+02, 3.460752E+02, 3.569765E+02, 3.679058E+02, 3.788657E+02,
3.898586E+02, 4.008872E+02, 4.119542E+02, 4.230620E+02, 4.342132E+02, 4.454103E+02,
4.566559E+02, 4.679523E+02, 4.793019E+02, 4.907071E+02, 5.021701E+02, 5.136932E+02,
5.252783E+02, 5.369277E+02, 5.486432E+02, 5.604270E+02, 5.722807E+02, 5.842062E+02,
5.962054E+02, 6.082797E+02, 6.204309E+02, 6.326605E+02, 6.449701E+02, 6.573609E+02,
6.698345E+02, 6.823922E+02, 6.950352E+02, 7.077648E+02, 7.205821E+02, 7.334884E+02,
7.464847E+02, 7.595720E+02, 7.727515E+02, 7.860240E+02, 7.993905E+02, 8.128519E+02,
8.264092E+02, 8.400631E+02, 8.538144E+02, 8.676641E+02, 8.816127E+02, 8.956611E+02,
9.098099E+02, 9.240599E+02, 9.384118E+02, 9.528660E+02, 9.674234E+02, 9.820845E+02,
9.968498E+02, 1.011720E+03, 1.026696E+03, 1.041777E+03, 1.056965E+03, 1.072260E+03,
1.087662E+03, 1.103173E+03, 1.118791E+03, 1.134519E+03, 1.150356E+03, 1.166303E+03,
1.182360E+03, 1.198527E+03, 1.214806E+03, 1.231195E+03, 1.247697E+03, 1.264311E+03,
1.281037E+03, 1.297877E+03, 1.314829E+03, 1.331896E+03, 1.349076E+03, 1.366370E+03,
1.383780E+03, 1.401304E+03, 1.418943E+03, 1.436699E+03, 1.454570E+03, 1.472557E+03,
1.490661E+03, 1.508882E+03, 1.527220E+03, 1.545676E+03, 1.564249E+03, 1.582941E+03,
1.601751E+03, 1.620679E+03, 1.639727E+03, 1.658894E+03, 1.678180E+03, 1.697586E+03,
1.717112E+03, 1.736758E+03, 1.756525E+03, 1.776412E+03, 1.796421E+03, 1.816551E+03,
1.836803E+03, 1.857176E+03, 1.877672E+03, 1.898290E+03, 1.919031E+03, 1.939894E+03,
1.960881E+03, 1.981991E+03, 2.003225E+03, 2.024582E+03, 2.046063E+03, 2.067669E+03,
2.089399E+03, 2.111255E+03, 2.133235E+03, 2.155340E+03, 2.177571E+03, 2.199928E+03,
2.222410E+03, 2.245019E+03, 2.267754E+03, 2.290615E+03, 2.313604E+03, 2.336719E+03,
2.359962E+03, 2.383332E+03, 2.406830E+03, 2.430456E+03, 2.454210E+03, 2.478092E+03,
2.502103E+03, 2.526243E+03, 2.550512E+03, 2.574909E+03, 2.599436E+03, 2.624093E+03,
2.648880E+03, 2.673796E+03, 2.698843E+03, 2.724020E+03, 2.749328E+03, 2.774766E+03,
2.800335E+03, 2.826036E+03, 2.851868E+03, 2.877831E+03, 2.903926E+03, 2.930154E+03,
2.956513E+03, 2.983004E+03, 3.009628E+03, 3.036385E+03, 3.063274E+03, 3.090296E+03,
3.117452E+03, 3.144741E+03, 3.172163E+03, 3.199719E+03, 3.227409E+03, 3.255233E+03,
3.283191E+03, 3.311284E+03, 3.339510E+03, 3.367872E+03, 3.396369E+03, 3.425000E+03,
3.453767E+03, 3.482669E+03, 3.511706E+03, 3.540879E+03, 3.570188E+03, 3.599632E+03,
3.629213E+03, 3.658929E+03, 3.688783E+03, 3.718772E+03, 3.748898E+03, 3.779161E+03,
3.809561E+03, 3.840098E+03, 3.870771E+03, 3.901583E+03, 3.932531E+03, 3.963617E+03,
3.994841E+03, 4.026202E+03, 4.057701E+03, 4.089338E+03, 4.121113E+03, 4.153027E+03,
4.185079E+03, 4.217269E+03, 4.249598E+03, 4.282065E+03, 4.314671E+03, 4.347416E+03,
4.380300E+03, 4.413323E+03, 4.446485E+03, 4.479786E+03, 4.513226E+03, 4.546806E+03,
4.580526E+03, 4.614385E+03, 4.648383E+03, 4.682522E+03, 4.716800E+03, 4.751218E+03,
4.785776E+03, 4.820474E+03, 4.855312E+03, 4.890290E+03, 4.925409E+03, 4.960667E+03,
4.996067E+03, 5.031606E+03, 5.067286E+03, 5.103107E+03, 5.139068E+03, 5.175170E+03,
5.211412E+03, 5.247795E+03, 5.284319E+03, 5.320984E+03, 5.357790E+03, 5.394736E+03,
5.431824E+03, 5.469052E+03, 5.506421E+03, 5.543932E+03, 5.581583E+03, 5.619376E+03,
5.657310E+03, 5.695385E+03, 5.733601E+03, 5.771958E+03, 5.810456E+03, 5.849096E+03,
5.887876E+03, 5.926798E+03, 5.965862E+03, 6.005066E+03, 6.044412E+03, 6.083898E+03,
6.123527E+03, 6.163296E+03, 6.203206E+03, 6.243258E+03, 6.283451E+03, 6.323785E+03,
6.364261E+03, 6.404877E+03, 6.445635E+03, 6.486534E+03, 6.527574E+03, 6.568755E+03,
6.610077E+03, 6.651540E+03, 6.693144E+03, 6.734889E+03, 6.776775E+03, 6.818802E+03,
6.860969E+03, 6.903278E+03, 6.945727E+03, 6.988317E+03, 7.031048E+03, 7.073919E+03,
7.116931E+03,
])
# ---------------------- M = 15, I = 3 ---------------------------
M = 15
I = 3
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[1]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.200001E+01, 3.527472E+01, 6.604716E+01, 9.694745E+01, 1.278841E+02, 1.588401E+02,
1.898098E+02, 2.207909E+02, 2.517823E+02, 2.827832E+02, 3.137934E+02, 3.448129E+02,
3.758418E+02, 4.068811E+02, 4.379322E+02, 4.689977E+02, 5.000812E+02, 5.311880E+02,
5.623246E+02, 5.934988E+02, 6.247200E+02, 6.559986E+02, 6.873464E+02, 7.187757E+02,
7.502996E+02, 7.819318E+02, 8.136862E+02, 8.455769E+02, 8.776178E+02, 9.098230E+02,
9.422062E+02, 9.747808E+02, 1.007560E+03, 1.040555E+03, 1.073780E+03, 1.107246E+03,
1.140963E+03, 1.174943E+03, 1.209195E+03, 1.243729E+03, 1.278555E+03, 1.313680E+03,
1.349113E+03, 1.384862E+03, 1.420935E+03, 1.457337E+03, 1.494076E+03, 1.531157E+03,
1.568588E+03, 1.606372E+03, 1.644516E+03, 1.683025E+03, 1.721902E+03, 1.761153E+03,
1.800782E+03, 1.840792E+03, 1.881188E+03, 1.921974E+03, 1.963151E+03, 2.004725E+03,
2.046697E+03, 2.089072E+03, 2.131851E+03, 2.175038E+03, 2.218634E+03, 2.262643E+03,
2.307067E+03, 2.351908E+03, 2.397169E+03, 2.442851E+03, 2.488956E+03, 2.535486E+03,
2.582444E+03, 2.629831E+03, 2.677648E+03, 2.725898E+03, 2.774582E+03, 2.823702E+03,
2.873259E+03, 2.923254E+03, 2.973690E+03, 3.024567E+03, 3.075888E+03, 3.127652E+03,
3.179862E+03, 3.232519E+03, 3.285625E+03, 3.339179E+03, 3.393184E+03, 3.447641E+03,
3.502550E+03, 3.557914E+03, 3.613733E+03, 3.670008E+03, 3.726740E+03, 3.783930E+03,
3.841580E+03, 3.899690E+03, 3.958261E+03, 4.017295E+03, 4.076791E+03, 4.136753E+03,
4.197179E+03, 4.258071E+03, 4.319431E+03, 4.381258E+03, 4.443554E+03, 4.506320E+03,
4.569557E+03, 4.633264E+03, 4.697444E+03, 4.762098E+03, 4.827225E+03, 4.892827E+03,
4.958904E+03, 5.025457E+03, 5.092488E+03, 5.159997E+03, 5.227984E+03, 5.296451E+03,
5.365397E+03, 5.434825E+03, 5.504734E+03, 5.575125E+03, 5.646000E+03, 5.717358E+03,
5.789200E+03, 5.861527E+03, 5.934340E+03, 6.007640E+03, 6.081426E+03, 6.155700E+03,
6.230462E+03, 6.305713E+03, 6.381453E+03, 6.457684E+03, 6.534405E+03, 6.611617E+03,
6.689320E+03, 6.767516E+03, 6.846205E+03, 6.925387E+03, 7.005063E+03, 7.085233E+03,
7.165898E+03, 7.247058E+03, 7.328713E+03, 7.410865E+03, 7.493514E+03, 7.576660E+03,
7.660303E+03, 7.744444E+03, 7.829083E+03, 7.914221E+03, 7.999858E+03, 8.085994E+03,
8.172630E+03, 8.259766E+03, 8.347402E+03, 8.435540E+03, 8.524178E+03, 8.613317E+03,
8.702958E+03, 8.793101E+03, 8.883746E+03, 8.974893E+03, 9.066542E+03, 9.158695E+03,
9.251350E+03, 9.344509E+03, 9.438170E+03, 9.532336E+03, 9.627005E+03, 9.722177E+03,
9.817854E+03, 9.914034E+03, 1.001072E+04, 1.010791E+04, 1.020560E+04, 1.030380E+04,
1.040250E+04, 1.050171E+04, 1.060142E+04, 1.070163E+04, 1.080235E+04, 1.090358E+04,
1.100530E+04, 1.110754E+04, 1.121027E+04, 1.131352E+04, 1.141726E+04, 1.152151E+04,
1.162627E+04, 1.173152E+04, 1.183728E+04, 1.194355E+04, 1.205032E+04, 1.215759E+04,
1.226537E+04, 1.237364E+04, 1.248242E+04, 1.259171E+04, 1.270149E+04, 1.281178E+04,
1.292257E+04, 1.303386E+04, 1.314566E+04, 1.325795E+04, 1.337075E+04, 1.348404E+04,
1.359784E+04, 1.371213E+04, 1.382693E+04, 1.394222E+04, 1.405802E+04, 1.417431E+04,
1.429110E+04, 1.440838E+04, 1.452617E+04, 1.464445E+04, 1.476323E+04, 1.488250E+04,
1.500227E+04, 1.512254E+04, 1.524329E+04, 1.536455E+04, 1.548629E+04, 1.560853E+04,
1.573127E+04, 1.585449E+04, 1.597821E+04, 1.610241E+04, 1.622711E+04, 1.635230E+04,
1.647798E+04, 1.660414E+04, 1.673080E+04, 1.685794E+04, 1.698556E+04, 1.711368E+04,
1.724228E+04, 1.737136E+04, 1.750093E+04, 1.763099E+04, 1.776152E+04, 1.789254E+04,
1.802404E+04, 1.815602E+04, 1.828848E+04, 1.842142E+04, 1.855484E+04, 1.868874E+04,
1.882311E+04, 1.895796E+04, 1.909329E+04, 1.922909E+04, 1.936536E+04, 1.950211E+04,
1.963933E+04, 1.977703E+04, 1.991519E+04, 2.005382E+04, 2.019293E+04, 2.033250E+04,
2.047254E+04, 2.061304E+04, 2.075401E+04, 2.089545E+04, 2.103735E+04, 2.117971E+04,
2.132254E+04, 2.146582E+04, 2.160957E+04, 2.175378E+04, 2.189844E+04, 2.204356E+04,
2.218914E+04, 2.233518E+04, 2.248166E+04, 2.262861E+04, 2.277600E+04, 2.292385E+04,
2.307215E+04, 2.322090E+04, 2.337009E+04, 2.351974E+04, 2.366983E+04, 2.382036E+04,
2.397134E+04, 2.412277E+04, 2.427464E+04, 2.442694E+04, 2.457969E+04, 2.473288E+04,
2.488651E+04, 2.504057E+04, 2.519507E+04, 2.535001E+04, 2.550537E+04, 2.566118E+04,
2.581741E+04,
])
# ---------------------- M = 15, I = 4 ---------------------------
M = 15
I = 4
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[1]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.200001E+01, 3.536447E+01, 6.622832E+01, 9.721965E+01, 1.282473E+02, 1.592942E+02,
1.903549E+02, 2.214270E+02, 2.525093E+02, 2.836013E+02, 3.147026E+02, 3.458131E+02,
3.769332E+02, 4.080636E+02, 4.392059E+02, 4.703627E+02, 5.015378E+02, 5.327363E+02,
5.639649E+02, 5.952315E+02, 6.265456E+02, 6.579177E+02, 6.893595E+02, 7.208836E+02,
7.525032E+02, 7.842320E+02, 8.160840E+02, 8.480734E+02, 8.802143E+02, 9.125207E+02,
9.450063E+02, 9.776848E+02, 1.010569E+03, 1.043672E+03, 1.077005E+03, 1.110580E+03,
1.144409E+03, 1.178502E+03, 1.212869E+03, 1.247520E+03, 1.282464E+03, 1.317709E+03,
1.353264E+03, 1.389137E+03, 1.425335E+03, 1.461864E+03, 1.498732E+03, 1.535944E+03,
1.573507E+03, 1.611425E+03, 1.649705E+03, 1.688351E+03, 1.727367E+03, 1.766759E+03,
1.806529E+03, 1.846683E+03, 1.887223E+03, 1.928154E+03, 1.969478E+03, 2.011199E+03,
2.053320E+03, 2.095842E+03, 2.138770E+03, 2.182105E+03, 2.225850E+03, 2.270007E+03,
2.314578E+03, 2.359564E+03, 2.404969E+03, 2.450792E+03, 2.497037E+03, 2.543704E+03,
2.590795E+03, 2.638311E+03, 2.686254E+03, 2.734623E+03, 2.783422E+03, 2.832649E+03,
2.882307E+03, 2.932396E+03, 2.982916E+03, 3.033868E+03, 3.085254E+03, 3.137072E+03,
3.189325E+03, 3.242011E+03, 3.295131E+03, 3.348686E+03, 3.402676E+03, 3.457100E+03,
3.511959E+03, 3.567253E+03, 3.622981E+03, 3.679143E+03, 3.735740E+03, 3.792771E+03,
3.850235E+03, 3.908133E+03, 3.966464E+03, 4.025226E+03, 4.084421E+03, 4.144048E+03,
4.204105E+03, 4.264592E+03, 4.325508E+03, 4.386854E+03, 4.448627E+03, 4.510828E+03,
4.573454E+03, 4.636507E+03, 4.699984E+03, 4.763884E+03, 4.828207E+03, 4.892952E+03,
4.958117E+03, 5.023701E+03, 5.089704E+03, 5.156124E+03, 5.222959E+03, 5.290210E+03,
5.357874E+03, 5.425950E+03, 5.494437E+03, 5.563333E+03, 5.632638E+03, 5.702349E+03,
5.772466E+03, 5.842987E+03, 5.913910E+03, 5.985234E+03, 6.056957E+03, 6.129079E+03,
6.201596E+03, 6.274509E+03, 6.347814E+03, 6.421511E+03, 6.495598E+03, 6.570073E+03,
6.644935E+03, 6.720182E+03, 6.795811E+03, 6.871822E+03, 6.948213E+03, 7.024981E+03,
7.102125E+03, 7.179643E+03, 7.257534E+03, 7.335796E+03, 7.414426E+03, 7.493422E+03,
7.572784E+03, 7.652509E+03, 7.732594E+03, 7.813039E+03, 7.893841E+03, 7.974999E+03,
8.056509E+03, 8.138371E+03, 8.220583E+03, 8.303141E+03, 8.386045E+03, 8.469292E+03,
8.552881E+03, 8.636808E+03, 8.721073E+03, 8.805673E+03, 8.890606E+03, 8.975870E+03,
9.061463E+03, 9.147383E+03, 9.233628E+03, 9.320195E+03, 9.407082E+03, 9.494288E+03,
9.581810E+03, 9.669647E+03, 9.757795E+03, 9.846253E+03, 9.935019E+03, 1.002409E+04,
1.011347E+04, 1.020314E+04, 1.029312E+04, 1.038339E+04, 1.047396E+04, 1.056482E+04,
1.065597E+04, 1.074740E+04, 1.083913E+04, 1.093114E+04, 1.102343E+04, 1.111600E+04,
1.120884E+04, 1.130197E+04, 1.139536E+04, 1.148903E+04, 1.158296E+04, 1.167716E+04,
1.177163E+04, 1.186635E+04, 1.196134E+04, 1.205658E+04, 1.215208E+04, 1.224784E+04,
1.234384E+04, 1.244010E+04, 1.253660E+04, 1.263334E+04, 1.273033E+04, 1.282756E+04,
1.292502E+04, 1.302272E+04, 1.312066E+04, 1.321883E+04, 1.331722E+04, 1.341585E+04,
1.351469E+04, 1.361377E+04, 1.371306E+04, 1.381257E+04, 1.391230E+04, 1.401224E+04,
1.411240E+04, 1.421276E+04, 1.431333E+04, 1.441411E+04, 1.451509E+04, 1.461628E+04,
1.471766E+04, 1.481925E+04, 1.492102E+04, 1.502299E+04, 1.512516E+04, 1.522751E+04,
1.533005E+04, 1.543277E+04, 1.553568E+04, 1.563877E+04, 1.574203E+04, 1.584548E+04,
1.594910E+04, 1.605289E+04, 1.615685E+04, 1.626099E+04, 1.636528E+04, 1.646975E+04,
1.657438E+04, 1.667916E+04, 1.678411E+04, 1.688922E+04, 1.699448E+04, 1.709989E+04,
1.720545E+04, 1.731117E+04, 1.741703E+04, 1.752304E+04, 1.762919E+04, 1.773548E+04,
1.784191E+04, 1.794848E+04, 1.805519E+04, 1.816203E+04, 1.826900E+04, 1.837611E+04,
1.848334E+04, 1.859070E+04, 1.869818E+04, 1.880579E+04, 1.891352E+04, 1.902137E+04,
1.912934E+04, 1.923742E+04, 1.934562E+04, 1.945393E+04, 1.956235E+04, 1.967088E+04,
1.977952E+04, 1.988826E+04, 1.999711E+04, 2.010606E+04, 2.021511E+04, 2.032426E+04,
2.043350E+04, 2.054284E+04, 2.065228E+04, 2.076181E+04, 2.087143E+04, 2.098113E+04,
2.109093E+04, 2.120081E+04, 2.131078E+04, 2.142082E+04, 2.153095E+04, 2.164116E+04,
2.175144E+04, 2.186181E+04, 2.197224E+04, 2.208275E+04, 2.219333E+04, 2.230399E+04,
2.241471E+04,
])
# ---------------------- M = 16, I = 1 ---------------------------
M = 16
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[1]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
8.000000E+00, 1.634928E+01, 2.947690E+01, 4.274338E+01, 5.604397E+01, 6.935994E+01,
8.268533E+01, 9.601763E+01, 1.093556E+02, 1.226986E+02, 1.360462E+02, 1.493982E+02,
1.627544E+02, 1.761147E+02, 1.894792E+02, 2.028480E+02, 2.162214E+02, 2.295997E+02,
2.429839E+02, 2.563748E+02, 2.697738E+02, 2.831826E+02, 2.966031E+02, 3.100376E+02,
3.234889E+02, 3.369598E+02, 3.504537E+02, 3.639739E+02, 3.775240E+02, 3.911080E+02,
4.047298E+02, 4.183933E+02, 4.321026E+02, 4.458619E+02, 4.596752E+02, 4.735466E+02,
4.874801E+02, 5.014796E+02, 5.155490E+02, 5.296922E+02, 5.439127E+02, 5.582142E+02,
5.726001E+02, 5.870738E+02, 6.016384E+02, 6.162971E+02, 6.310528E+02, 6.459085E+02,
6.608669E+02, 6.759306E+02, 6.911022E+02, 7.063841E+02, 7.217786E+02, 7.372879E+02,
7.529142E+02, 7.686595E+02, 7.845258E+02, 8.005148E+02, 8.166285E+02, 8.328685E+02,
8.492364E+02, 8.657339E+02, 8.823623E+02, 8.991233E+02, 9.160180E+02, 9.330480E+02,
9.502144E+02, 9.675184E+02, 9.849613E+02, 1.002544E+03, 1.020268E+03, 1.038134E+03,
1.056143E+03, 1.074296E+03, 1.092594E+03, 1.111039E+03, 1.129630E+03, 1.148368E+03,
1.167255E+03, 1.186292E+03, 1.205478E+03, 1.224815E+03, 1.244304E+03, 1.263945E+03,
1.283738E+03, 1.303686E+03, 1.323787E+03, 1.344044E+03, 1.364456E+03, 1.385024E+03,
1.405748E+03, 1.426630E+03, 1.447670E+03, 1.468868E+03, 1.490225E+03, 1.511742E+03,
1.533418E+03, 1.555255E+03, 1.577253E+03, 1.599413E+03, 1.621734E+03, 1.644218E+03,
1.666865E+03, 1.689675E+03, 1.712649E+03, 1.735787E+03, 1.759090E+03, 1.782559E+03,
1.806192E+03, 1.829993E+03, 1.853959E+03, 1.878093E+03, 1.902393E+03, 1.926862E+03,
1.951499E+03, 1.976304E+03, 2.001279E+03, 2.026423E+03, 2.051737E+03, 2.077221E+03,
2.102875E+03, 2.128701E+03, 2.154698E+03, 2.180867E+03, 2.207208E+03, 2.233722E+03,
2.260408E+03, 2.287269E+03, 2.314302E+03, 2.341510E+03, 2.368893E+03, 2.396450E+03,
2.424183E+03, 2.452091E+03, 2.480175E+03, 2.508436E+03, 2.536873E+03, 2.565488E+03,
2.594280E+03, 2.623250E+03, 2.652398E+03, 2.681725E+03, 2.711231E+03, 2.740916E+03,
2.770781E+03, 2.800826E+03, 2.831052E+03, 2.861458E+03, 2.892045E+03, 2.922814E+03,
2.953765E+03, 2.984899E+03, 3.016214E+03, 3.047713E+03, 3.079395E+03, 3.111261E+03,
3.143311E+03, 3.175545E+03, 3.207964E+03, 3.240568E+03, 3.273358E+03, 3.306334E+03,
3.339495E+03, 3.372843E+03, 3.406378E+03, 3.440101E+03, 3.474010E+03, 3.508108E+03,
3.542394E+03, 3.576869E+03, 3.611532E+03, 3.646385E+03, 3.681428E+03, 3.716660E+03,
3.752083E+03, 3.787696E+03, 3.823500E+03, 3.859496E+03, 3.895683E+03, 3.932063E+03,
3.968634E+03, 4.005398E+03, 4.042355E+03, 4.079506E+03, 4.116850E+03, 4.154388E+03,
4.192120E+03, 4.230046E+03, 4.268168E+03, 4.306485E+03, 4.344997E+03, 4.383705E+03,
4.422609E+03, 4.461709E+03, 4.501007E+03, 4.540501E+03, 4.580192E+03, 4.620081E+03,
4.660168E+03, 4.700454E+03, 4.740937E+03, 4.781620E+03, 4.822501E+03, 4.863582E+03,
4.904862E+03, 4.946342E+03, 4.988023E+03, 5.029903E+03, 5.071985E+03, 5.114267E+03,
5.156751E+03, 5.199436E+03, 5.242323E+03, 5.285411E+03, 5.328702E+03, 5.372196E+03,
5.415892E+03, 5.459791E+03, 5.503893E+03, 5.548199E+03, 5.592708E+03, 5.637422E+03,
5.682339E+03, 5.727461E+03, 5.772787E+03, 5.818318E+03, 5.864053E+03, 5.909994E+03,
5.956141E+03, 6.002493E+03, 6.049051E+03, 6.095814E+03, 6.142784E+03, 6.189960E+03,
6.237343E+03, 6.284932E+03, 6.332728E+03, 6.380732E+03, 6.428942E+03, 6.477360E+03,
6.525985E+03, 6.574818E+03, 6.623859E+03, 6.673107E+03, 6.722564E+03, 6.772229E+03,
6.822102E+03, 6.872184E+03, 6.922475E+03, 6.972974E+03, 7.023682E+03, 7.074599E+03,
7.125725E+03, 7.177060E+03, 7.228605E+03, 7.280359E+03, 7.332322E+03, 7.384495E+03,
7.436877E+03, 7.489470E+03, 7.542272E+03, 7.595283E+03, 7.648505E+03, 7.701937E+03,
7.755579E+03, 7.809431E+03, 7.863493E+03, 7.917765E+03, 7.972247E+03, 8.026940E+03,
8.081842E+03, 8.136956E+03, 8.192279E+03, 8.247813E+03, 8.303557E+03, 8.359511E+03,
8.415676E+03, 8.472051E+03, 8.528637E+03, 8.585433E+03, 8.642439E+03, 8.699655E+03,
8.757082E+03, 8.814719E+03, 8.872566E+03, 8.930624E+03, 8.988891E+03, 9.047369E+03,
9.106056E+03, 9.164954E+03, 9.224061E+03, 9.283378E+03, 9.342905E+03, 9.402642E+03,
9.462588E+03, 9.522744E+03, 9.583109E+03, 9.643684E+03, 9.704468E+03, 9.765461E+03,
9.826663E+03,
])
# ---------------------- M = 16, I = 2 ---------------------------
M = 16
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[1]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
8.000000E+00, 1.635327E+01, 2.948508E+01, 4.275570E+01, 5.606042E+01, 6.938053E+01,
8.271004E+01, 9.604648E+01, 1.093886E+02, 1.227357E+02, 1.360875E+02, 1.494436E+02,
1.628039E+02, 1.761684E+02, 1.895370E+02, 2.029099E+02, 2.162874E+02, 2.296699E+02,
2.430582E+02, 2.564533E+02, 2.698565E+02, 2.832694E+02, 2.966941E+02, 3.101328E+02,
3.235883E+02, 3.370634E+02, 3.505615E+02, 3.640860E+02, 3.776405E+02, 3.912288E+02,
4.048549E+02, 4.185229E+02, 4.322366E+02, 4.460004E+02, 4.598182E+02, 4.736942E+02,
4.876323E+02, 5.016365E+02, 5.157107E+02, 5.298586E+02, 5.440840E+02, 5.583904E+02,
5.727812E+02, 5.872599E+02, 6.018296E+02, 6.164934E+02, 6.312544E+02, 6.461153E+02,
6.610791E+02, 6.761482E+02, 6.913252E+02, 7.066126E+02, 7.220127E+02, 7.375277E+02,
7.531597E+02, 7.689108E+02, 7.847830E+02, 8.007780E+02, 8.168977E+02, 8.331437E+02,
8.495178E+02, 8.660215E+02, 8.826562E+02, 8.994235E+02, 9.163248E+02, 9.333612E+02,
9.505342E+02, 9.678449E+02, 9.852945E+02, 1.002884E+03, 1.020615E+03, 1.038488E+03,
1.056504E+03, 1.074664E+03, 1.092970E+03, 1.111421E+03, 1.130019E+03, 1.148765E+03,
1.167660E+03, 1.186704E+03, 1.205898E+03, 1.225242E+03, 1.244739E+03, 1.264388E+03,
1.284189E+03, 1.304145E+03, 1.324254E+03, 1.344519E+03, 1.364939E+03, 1.385515E+03,
1.406248E+03, 1.427139E+03, 1.448187E+03, 1.469393E+03, 1.490759E+03, 1.512284E+03,
1.533969E+03, 1.555815E+03, 1.577822E+03, 1.599990E+03, 1.622320E+03, 1.644813E+03,
1.667469E+03, 1.690288E+03, 1.713271E+03, 1.736419E+03, 1.759731E+03, 1.783208E+03,
1.806851E+03, 1.830660E+03, 1.854636E+03, 1.878779E+03, 1.903089E+03, 1.927566E+03,
1.952212E+03, 1.977026E+03, 2.002010E+03, 2.027162E+03, 2.052485E+03, 2.077977E+03,
2.103640E+03, 2.129474E+03, 2.155479E+03, 2.181656E+03, 2.208005E+03, 2.234526E+03,
2.261220E+03, 2.288087E+03, 2.315127E+03, 2.342341E+03, 2.369730E+03, 2.397293E+03,
2.425030E+03, 2.452943E+03, 2.481032E+03, 2.509296E+03, 2.537737E+03, 2.566354E+03,
2.595148E+03, 2.624119E+03, 2.653268E+03, 2.682595E+03, 2.712100E+03, 2.741783E+03,
2.771646E+03, 2.801687E+03, 2.831908E+03, 2.862308E+03, 2.892889E+03, 2.923650E+03,
2.954592E+03, 2.985715E+03, 3.017019E+03, 3.048505E+03, 3.080172E+03, 3.112022E+03,
3.144054E+03, 3.176269E+03, 3.208667E+03, 3.241248E+03, 3.274013E+03, 3.306962E+03,
3.340095E+03, 3.373412E+03, 3.406913E+03, 3.440600E+03, 3.474472E+03, 3.508529E+03,
3.542772E+03, 3.577201E+03, 3.611816E+03, 3.646618E+03, 3.681606E+03, 3.716781E+03,
3.752144E+03, 3.787693E+03, 3.823430E+03, 3.859355E+03, 3.895469E+03, 3.931770E+03,
3.968260E+03, 4.004938E+03, 4.041806E+03, 4.078862E+03, 4.116108E+03, 4.153544E+03,
4.191169E+03, 4.228984E+03, 4.266989E+03, 4.305184E+03, 4.343570E+03, 4.382146E+03,
4.420914E+03, 4.459872E+03, 4.499021E+03, 4.538362E+03, 4.577894E+03, 4.617618E+03,
4.657534E+03, 4.697641E+03, 4.737941E+03, 4.778433E+03, 4.819117E+03, 4.859994E+03,
4.901064E+03, 4.942326E+03, 4.983781E+03, 5.025429E+03, 5.067270E+03, 5.109305E+03,
5.151533E+03, 5.193954E+03, 5.236569E+03, 5.279378E+03, 5.322380E+03, 5.365577E+03,
5.408967E+03, 5.452551E+03, 5.496329E+03, 5.540302E+03, 5.584469E+03, 5.628830E+03,
5.673385E+03, 5.718135E+03, 5.763080E+03, 5.808219E+03, 5.853552E+03, 5.899081E+03,
5.944804E+03, 5.990722E+03, 6.036834E+03, 6.083141E+03, 6.129644E+03, 6.176341E+03,
6.223233E+03, 6.270319E+03, 6.317601E+03, 6.365078E+03, 6.412749E+03, 6.460616E+03,
6.508677E+03, 6.556933E+03, 6.605384E+03, 6.654030E+03, 6.702871E+03, 6.751907E+03,
6.801137E+03, 6.850563E+03, 6.900183E+03, 6.949997E+03, 7.000007E+03, 7.050211E+03,
7.100609E+03, 7.151202E+03, 7.201990E+03, 7.252972E+03, 7.304148E+03, 7.355518E+03,
7.407083E+03, 7.458842E+03, 7.510794E+03, 7.562941E+03, 7.615282E+03, 7.667816E+03,
7.720544E+03, 7.773466E+03, 7.826581E+03, 7.879889E+03, 7.933391E+03, 7.987085E+03,
8.040973E+03, 8.095054E+03, 8.149327E+03, 8.203793E+03, 8.258452E+03, 8.313303E+03,
8.368346E+03, 8.423581E+03, 8.479008E+03, 8.534627E+03, 8.590438E+03, 8.646440E+03,
8.702634E+03, 8.759019E+03, 8.815594E+03, 8.872361E+03, 8.929318E+03, 8.986466E+03,
9.043803E+03, 9.101331E+03, 9.159049E+03, 9.216957E+03, 9.275054E+03, 9.333341E+03,
9.391817E+03, 9.450481E+03, 9.509335E+03, 9.568377E+03, 9.627607E+03, 9.687025E+03,
9.746631E+03,
])
# ---------------------- M = 16, I = 3 ---------------------------
M = 16
I = 3
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[1]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.200018E+01, 4.353101E+01, 8.267877E+01, 1.219284E+02, 1.612106E+02, 2.005120E+02,
2.398284E+02, 2.791580E+02, 3.184998E+02, 3.578535E+02, 3.972192E+02, 4.365975E+02,
4.759903E+02, 5.154010E+02, 5.548351E+02, 5.943007E+02, 6.338083E+02, 6.733713E+02,
7.130053E+02, 7.527282E+02, 7.925598E+02, 8.325212E+02, 8.726345E+02, 9.129224E+02,
9.534080E+02, 9.941141E+02, 1.035064E+03, 1.076278E+03, 1.117780E+03, 1.159590E+03,
1.201727E+03, 1.244211E+03, 1.287059E+03, 1.330289E+03, 1.373916E+03, 1.417957E+03,
1.462425E+03, 1.507334E+03, 1.552696E+03, 1.598524E+03, 1.644828E+03, 1.691619E+03,
1.738907E+03, 1.786701E+03, 1.835009E+03, 1.883840E+03, 1.933201E+03, 1.983100E+03,
2.033542E+03, 2.084536E+03, 2.136085E+03, 2.188197E+03, 2.240876E+03, 2.294127E+03,
2.347956E+03, 2.402366E+03, 2.457362E+03, 2.512948E+03, 2.569127E+03, 2.625905E+03,
2.683283E+03, 2.741265E+03, 2.799856E+03, 2.859056E+03, 2.918871E+03, 2.979301E+03,
3.040351E+03, 3.102023E+03, 3.164318E+03, 3.227241E+03, 3.290792E+03, 3.354975E+03,
3.419791E+03, 3.485243E+03, 3.551333E+03, 3.618062E+03, 3.685434E+03, 3.753448E+03,
3.822109E+03, 3.891417E+03, 3.961373E+03, 4.031981E+03, 4.103242E+03, 4.175156E+03,
4.247727E+03, 4.320955E+03, 4.394842E+03, 4.469390E+03, 4.544599E+03, 4.620473E+03,
4.697012E+03, 4.774217E+03, 4.852090E+03, 4.930633E+03, 5.009846E+03, 5.089732E+03,
5.170291E+03, 5.251524E+03, 5.333434E+03, 5.416021E+03, 5.499287E+03, 5.583232E+03,
5.667858E+03, 5.753167E+03, 5.839159E+03, 5.925836E+03, 6.013198E+03, 6.101247E+03,
6.189984E+03, 6.279409E+03, 6.369525E+03, 6.460332E+03, 6.551831E+03, 6.644024E+03,
6.736910E+03, 6.830491E+03, 6.924769E+03, 7.019743E+03, 7.115415E+03, 7.211786E+03,
7.308857E+03, 7.406628E+03, 7.505101E+03, 7.604275E+03, 7.704153E+03, 7.804734E+03,
7.906020E+03, 8.008011E+03, 8.110707E+03, 8.214111E+03, 8.318221E+03, 8.423040E+03,
8.528567E+03, 8.634803E+03, 8.741749E+03, 8.849406E+03, 8.957773E+03, 9.066852E+03,
9.176642E+03, 9.287145E+03, 9.398361E+03, 9.510290E+03, 9.622933E+03, 9.736290E+03,
9.850361E+03, 9.965148E+03, 1.008065E+04, 1.019687E+04, 1.031380E+04, 1.043145E+04,
1.054981E+04, 1.066889E+04, 1.078869E+04, 1.090921E+04, 1.103044E+04, 1.115239E+04,
1.127506E+04, 1.139844E+04, 1.152254E+04, 1.164736E+04, 1.177290E+04, 1.189916E+04,
1.202613E+04, 1.215382E+04, 1.228223E+04, 1.241135E+04, 1.254120E+04, 1.267176E+04,
1.280303E+04, 1.293503E+04, 1.306774E+04, 1.320117E+04, 1.333531E+04, 1.347018E+04,
1.360575E+04, 1.374205E+04, 1.387906E+04, 1.401678E+04, 1.415522E+04, 1.429438E+04,
1.443424E+04, 1.457483E+04, 1.471612E+04, 1.485813E+04, 1.500085E+04, 1.514429E+04,
1.528843E+04, 1.543329E+04, 1.557886E+04, 1.572514E+04, 1.587213E+04, 1.601983E+04,
1.616823E+04, 1.631735E+04, 1.646717E+04, 1.661770E+04, 1.676894E+04, 1.692088E+04,
1.707353E+04, 1.722688E+04, 1.738094E+04, 1.753570E+04, 1.769116E+04, 1.784733E+04,
1.800419E+04, 1.816176E+04, 1.832002E+04, 1.847899E+04, 1.863865E+04, 1.879901E+04,
1.896006E+04, 1.912181E+04, 1.928426E+04, 1.944740E+04, 1.961123E+04, 1.977575E+04,
1.994097E+04, 2.010688E+04, 2.027347E+04, 2.044076E+04, 2.060873E+04, 2.077739E+04,
2.094673E+04, 2.111676E+04, 2.128747E+04, 2.145887E+04, 2.163095E+04, 2.180371E+04,
2.197714E+04, 2.215126E+04, 2.232606E+04, 2.250153E+04, 2.267767E+04, 2.285449E+04,
2.303199E+04, 2.321016E+04, 2.338899E+04, 2.356850E+04, 2.374868E+04, 2.392952E+04,
2.411104E+04, 2.429321E+04, 2.447606E+04, 2.465956E+04, 2.484373E+04, 2.502856E+04,
2.521404E+04, 2.540019E+04, 2.558699E+04, 2.577445E+04, 2.596257E+04, 2.615133E+04,
2.634075E+04, 2.653083E+04, 2.672155E+04, 2.691292E+04, 2.710493E+04, 2.729760E+04,
2.749091E+04, 2.768486E+04, 2.787945E+04, 2.807469E+04, 2.827056E+04, 2.846707E+04,
2.866422E+04, 2.886201E+04, 2.906042E+04, 2.925948E+04, 2.945916E+04, 2.965947E+04,
2.986042E+04, 3.006199E+04, 3.026418E+04, 3.046700E+04, 3.067045E+04, 3.087451E+04,
3.107920E+04, 3.128450E+04, 3.149043E+04, 3.169697E+04, 3.190412E+04, 3.211189E+04,
3.232027E+04, 3.252925E+04, 3.273885E+04, 3.294906E+04, 3.315987E+04, 3.337129E+04,
3.358331E+04, 3.379593E+04, 3.400915E+04, 3.422297E+04, 3.443739E+04, 3.465240E+04,
3.486801E+04, 3.508421E+04, 3.530100E+04, 3.551839E+04, 3.573636E+04, 3.595491E+04,
3.617405E+04,
])
# ---------------------- M = 16, I = 4 ---------------------------
M = 16
I = 4
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[1]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.200018E+01, 4.355487E+01, 8.272675E+01, 1.220005E+02, 1.613067E+02, 2.006322E+02,
2.399726E+02, 2.793263E+02, 3.186922E+02, 3.580700E+02, 3.974597E+02, 4.368621E+02,
4.762790E+02, 5.157139E+02, 5.551722E+02, 5.946621E+02, 6.341940E+02, 6.737815E+02,
7.134401E+02, 7.531878E+02, 7.930444E+02, 8.330310E+02, 8.731698E+02, 9.134835E+02,
9.539953E+02, 9.947280E+02, 1.035704E+03, 1.076947E+03, 1.118476E+03, 1.160314E+03,
1.202480E+03, 1.244992E+03, 1.287870E+03, 1.331130E+03, 1.374788E+03, 1.418860E+03,
1.463360E+03, 1.508301E+03, 1.553696E+03, 1.599557E+03, 1.645895E+03, 1.692720E+03,
1.740043E+03, 1.787872E+03, 1.836217E+03, 1.885084E+03, 1.934483E+03, 1.984419E+03,
2.034900E+03, 2.085932E+03, 2.137521E+03, 2.189673E+03, 2.242393E+03, 2.295685E+03,
2.349556E+03, 2.404008E+03, 2.459047E+03, 2.514677E+03, 2.570901E+03, 2.627723E+03,
2.685146E+03, 2.743175E+03, 2.801812E+03, 2.861060E+03, 2.920922E+03, 2.981401E+03,
3.042499E+03, 3.104220E+03, 3.166566E+03, 3.229539E+03, 3.293142E+03, 3.357377E+03,
3.422246E+03, 3.487751E+03, 3.553894E+03, 3.620678E+03, 3.688104E+03, 3.756175E+03,
3.824891E+03, 3.894256E+03, 3.964270E+03, 4.034936E+03, 4.106255E+03, 4.178229E+03,
4.250859E+03, 4.324148E+03, 4.398096E+03, 4.472705E+03, 4.547977E+03, 4.623914E+03,
4.700516E+03, 4.777785E+03, 4.855723E+03, 4.934331E+03, 5.013611E+03, 5.093563E+03,
5.174189E+03, 5.255491E+03, 5.337469E+03, 5.420125E+03, 5.503460E+03, 5.587476E+03,
5.672173E+03, 5.757554E+03, 5.843618E+03, 5.930367E+03, 6.017803E+03, 6.105926E+03,
6.194737E+03, 6.284239E+03, 6.374431E+03, 6.465314E+03, 6.556890E+03, 6.649160E+03,
6.742125E+03, 6.835786E+03, 6.930143E+03, 7.025198E+03, 7.120951E+03, 7.217404E+03,
7.314556E+03, 7.412410E+03, 7.510966E+03, 7.610225E+03, 7.710187E+03, 7.810854E+03,
7.912226E+03, 8.014303E+03, 8.117087E+03, 8.220578E+03, 8.324778E+03, 8.429685E+03,
8.535302E+03, 8.641629E+03, 8.748666E+03, 8.856414E+03, 8.964874E+03, 9.074045E+03,
9.183929E+03, 9.294526E+03, 9.405837E+03, 9.517861E+03, 9.630600E+03, 9.744054E+03,
9.858222E+03, 9.973107E+03, 1.008871E+04, 1.020502E+04, 1.032206E+04, 1.043980E+04,
1.055827E+04, 1.067745E+04, 1.079735E+04, 1.091797E+04, 1.103931E+04, 1.116136E+04,
1.128413E+04, 1.140762E+04, 1.153183E+04, 1.165676E+04, 1.178240E+04, 1.190877E+04,
1.203585E+04, 1.216365E+04, 1.229217E+04, 1.242140E+04, 1.255136E+04, 1.268203E+04,
1.281342E+04, 1.294553E+04, 1.307835E+04, 1.321189E+04, 1.334615E+04, 1.348113E+04,
1.361683E+04, 1.375324E+04, 1.389036E+04, 1.402821E+04, 1.416676E+04, 1.430604E+04,
1.444603E+04, 1.458673E+04, 1.472815E+04, 1.487028E+04, 1.501313E+04, 1.515669E+04,
1.530096E+04, 1.544594E+04, 1.559163E+04, 1.573804E+04, 1.588516E+04, 1.603299E+04,
1.618152E+04, 1.633077E+04, 1.648072E+04, 1.663138E+04, 1.678275E+04, 1.693483E+04,
1.708761E+04, 1.724110E+04, 1.739530E+04, 1.755019E+04, 1.770579E+04, 1.786210E+04,
1.801910E+04, 1.817681E+04, 1.833522E+04, 1.849432E+04, 1.865413E+04, 1.881464E+04,
1.897584E+04, 1.913774E+04, 1.930033E+04, 1.946362E+04, 1.962761E+04, 1.979228E+04,
1.995766E+04, 2.012372E+04, 2.029047E+04, 2.045791E+04, 2.062605E+04, 2.079487E+04,
2.096438E+04, 2.113457E+04, 2.130545E+04, 2.147701E+04, 2.164926E+04, 2.182219E+04,
2.199580E+04, 2.217009E+04, 2.234507E+04, 2.252072E+04, 2.269704E+04, 2.287405E+04,
2.305173E+04, 2.323008E+04, 2.340911E+04, 2.358881E+04, 2.376918E+04, 2.395022E+04,
2.413193E+04, 2.431431E+04, 2.449736E+04, 2.468107E+04, 2.486545E+04, 2.505049E+04,
2.523619E+04, 2.542255E+04, 2.560958E+04, 2.579726E+04, 2.598560E+04, 2.617460E+04,
2.636425E+04, 2.655456E+04, 2.674552E+04, 2.693713E+04, 2.712940E+04, 2.732231E+04,
2.751587E+04, 2.771008E+04, 2.790494E+04, 2.810044E+04, 2.829658E+04, 2.849337E+04,
2.869080E+04, 2.888886E+04, 2.908757E+04, 2.928691E+04, 2.948689E+04, 2.968751E+04,
2.988876E+04, 3.009064E+04, 3.029315E+04, 3.049629E+04, 3.070006E+04, 3.090446E+04,
3.110948E+04, 3.131513E+04, 3.152140E+04, 3.172830E+04, 3.193581E+04, 3.214394E+04,
3.235270E+04, 3.256206E+04, 3.277205E+04, 3.298265E+04, 3.319386E+04, 3.340568E+04,
3.361811E+04, 3.383115E+04, 3.404480E+04, 3.425905E+04, 3.447391E+04, 3.468937E+04,
3.490544E+04, 3.512210E+04, 3.533937E+04, 3.555723E+04, 3.577568E+04, 3.599474E+04,
3.621438E+04,
])
# ---------------------- M = 17, I = 1 ---------------------------
M = 17
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[1]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.200000E+01, 3.036750E+01, 5.612178E+01, 8.203022E+01, 1.079799E+02, 1.339502E+02,
1.599342E+02, 1.859291E+02, 2.119337E+02, 2.379470E+02, 2.639687E+02, 2.899986E+02,
3.160366E+02, 3.420830E+02, 3.681386E+02, 3.942045E+02, 4.202826E+02, 4.463759E+02,
4.724880E+02, 4.986238E+02, 5.247889E+02, 5.509901E+02, 5.772350E+02, 6.035318E+02,
6.298897E+02, 6.563182E+02, 6.828272E+02, 7.094270E+02, 7.361281E+02, 7.629408E+02,
7.898758E+02, 8.169433E+02, 8.441535E+02, 8.715165E+02, 8.990419E+02, 9.267392E+02,
9.546175E+02, 9.826856E+02, 1.010952E+03, 1.039424E+03, 1.068111E+03, 1.097019E+03,
1.126156E+03, 1.155527E+03, 1.185141E+03, 1.215002E+03, 1.245116E+03, 1.275490E+03,
1.306128E+03, 1.337035E+03, 1.368216E+03, 1.399675E+03, 1.431417E+03, 1.463446E+03,
1.495766E+03, 1.528380E+03, 1.561292E+03, 1.594505E+03, 1.628022E+03, 1.661847E+03,
1.695982E+03, 1.730431E+03, 1.765195E+03, 1.800278E+03, 1.835682E+03, 1.871408E+03,
1.907461E+03, 1.943841E+03, 1.980550E+03, 2.017592E+03, 2.054967E+03, 2.092677E+03,
2.130725E+03, 2.169112E+03, 2.207840E+03, 2.246911E+03, 2.286325E+03, 2.326085E+03,
2.366192E+03, 2.406647E+03, 2.447452E+03, 2.488609E+03, 2.530118E+03, 2.571981E+03,
2.614199E+03, 2.656774E+03, 2.699706E+03, 2.742997E+03, 2.786647E+03, 2.830659E+03,
2.875033E+03, 2.919770E+03, 2.964871E+03, 3.010338E+03, 3.056170E+03, 3.102370E+03,
3.148938E+03, 3.195875E+03, 3.243182E+03, 3.290860E+03, 3.338910E+03, 3.387332E+03,
3.436128E+03, 3.485298E+03, 3.534843E+03, 3.584764E+03, 3.635061E+03, 3.685735E+03,
3.736788E+03, 3.788220E+03, 3.840030E+03, 3.892221E+03, 3.944793E+03, 3.997746E+03,
4.051081E+03, 4.104799E+03, 4.158899E+03, 4.213384E+03, 4.268253E+03, 4.323507E+03,
4.379146E+03, 4.435171E+03, 4.491582E+03, 4.548380E+03, 4.605566E+03, 4.663139E+03,
4.721100E+03, 4.779450E+03, 4.838189E+03, 4.897317E+03, 4.956835E+03, 5.016743E+03,
5.077042E+03, 5.137731E+03, 5.198811E+03, 5.260282E+03, 5.322144E+03, 5.384399E+03,
5.447045E+03, 5.510084E+03, 5.573515E+03, 5.637339E+03, 5.701556E+03, 5.766165E+03,
5.831168E+03, 5.896563E+03, 5.962352E+03, 6.028535E+03, 6.095111E+03, 6.162081E+03,
6.229444E+03, 6.297201E+03, 6.365351E+03, 6.433896E+03, 6.502834E+03, 6.572165E+03,
6.641891E+03, 6.712010E+03, 6.782522E+03, 6.853428E+03, 6.924728E+03, 6.996420E+03,
7.068506E+03, 7.140985E+03, 7.213857E+03, 7.287122E+03, 7.360779E+03, 7.434829E+03,
7.509271E+03, 7.584105E+03, 7.659331E+03, 7.734948E+03, 7.810957E+03, 7.887357E+03,
7.964148E+03, 8.041329E+03, 8.118901E+03, 8.196862E+03, 8.275214E+03, 8.353954E+03,
8.433084E+03, 8.512603E+03, 8.592509E+03, 8.672804E+03, 8.753486E+03, 8.834556E+03,
8.916012E+03, 8.997855E+03, 9.080084E+03, 9.162699E+03, 9.245698E+03, 9.329082E+03,
9.412851E+03, 9.497003E+03, 9.581539E+03, 9.666457E+03, 9.751757E+03, 9.837440E+03,
9.923503E+03, 1.000995E+04, 1.009677E+04, 1.018398E+04, 1.027156E+04, 1.035952E+04,
1.044786E+04, 1.053658E+04, 1.062567E+04, 1.071515E+04, 1.080499E+04, 1.089521E+04,
1.098581E+04, 1.107678E+04, 1.116812E+04, 1.125984E+04, 1.135192E+04, 1.144438E+04,
1.153721E+04, 1.163041E+04, 1.172398E+04, 1.181791E+04, 1.191222E+04, 1.200689E+04,
1.210193E+04, 1.219733E+04, 1.229310E+04, 1.238923E+04, 1.248573E+04, 1.258259E+04,
1.267981E+04, 1.277739E+04, 1.287533E+04, 1.297363E+04, 1.307230E+04, 1.317132E+04,
1.327069E+04, 1.337043E+04, 1.347052E+04, 1.357096E+04, 1.367176E+04, 1.377291E+04,
1.387441E+04, 1.397627E+04, 1.407848E+04, 1.418103E+04, 1.428394E+04, 1.438719E+04,
1.449080E+04, 1.459474E+04, 1.469904E+04, 1.480368E+04, 1.490866E+04, 1.501399E+04,
1.511966E+04, 1.522567E+04, 1.533202E+04, 1.543871E+04, 1.554573E+04, 1.565310E+04,
1.576080E+04, 1.586884E+04, 1.597722E+04, 1.608592E+04, 1.619497E+04, 1.630434E+04,
1.641404E+04, 1.652408E+04, 1.663444E+04, 1.674513E+04, 1.685615E+04, 1.696750E+04,
1.707917E+04, 1.719117E+04, 1.730349E+04, 1.741613E+04, 1.752909E+04, 1.764238E+04,
1.775598E+04, 1.786991E+04, 1.798415E+04, 1.809870E+04, 1.821358E+04, 1.832876E+04,
1.844426E+04, 1.856008E+04, 1.867620E+04, 1.879264E+04, 1.890938E+04, 1.902644E+04,
1.914380E+04, 1.926147E+04, 1.937944E+04, 1.949772E+04, 1.961630E+04, 1.973518E+04,
1.985437E+04, 1.997385E+04, 2.009364E+04, 2.021372E+04, 2.033410E+04, 2.045477E+04,
2.057574E+04,
])
# ---------------------- M = 17, I = 2 ---------------------------
M = 17
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[1]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.800464E+01, 8.321075E+01, 1.599981E+02, 2.369086E+02, 3.138651E+02, 3.908526E+02,
4.678662E+02, 5.449041E+02, 6.219653E+02, 6.990504E+02, 7.761621E+02, 8.533068E+02,
9.304964E+02, 1.007750E+03, 1.085094E+03, 1.162563E+03, 1.240199E+03, 1.318049E+03,
1.396167E+03, 1.474609E+03, 1.553433E+03, 1.632699E+03, 1.712467E+03, 1.792793E+03,
1.873736E+03, 1.955349E+03, 2.037686E+03, 2.120794E+03, 2.204722E+03, 2.289512E+03,
2.375206E+03, 2.461843E+03, 2.549457E+03, 2.638082E+03, 2.727749E+03, 2.818487E+03,
2.910323E+03, 3.003280E+03, 3.097383E+03, 3.192652E+03, 3.289107E+03, 3.386767E+03,
3.485650E+03, 3.585770E+03, 3.687143E+03, 3.789784E+03, 3.893705E+03, 3.998918E+03,
4.105435E+03, 4.213267E+03, 4.322425E+03, 4.432917E+03, 4.544753E+03, 4.657942E+03,
4.772491E+03, 4.888409E+03, 5.005702E+03, 5.124379E+03, 5.244445E+03, 5.365907E+03,
5.488771E+03, 5.613043E+03, 5.738728E+03, 5.865832E+03, 5.994360E+03, 6.124317E+03,
6.255707E+03, 6.388535E+03, 6.522805E+03, 6.658522E+03, 6.795690E+03, 6.934312E+03,
7.074392E+03, 7.215933E+03, 7.358940E+03, 7.503415E+03, 7.649362E+03, 7.796783E+03,
7.945681E+03, 8.096060E+03, 8.247921E+03, 8.401268E+03, 8.556102E+03, 8.712426E+03,
8.870242E+03, 9.029552E+03, 9.190358E+03, 9.352662E+03, 9.516465E+03, 9.681769E+03,
9.848576E+03, 1.001689E+04, 1.018670E+04, 1.035802E+04, 1.053085E+04, 1.070519E+04,
1.088103E+04, 1.105839E+04, 1.123725E+04, 1.141763E+04, 1.159951E+04, 1.178291E+04,
1.196781E+04, 1.215423E+04, 1.234216E+04, 1.253160E+04, 1.272255E+04, 1.291501E+04,
1.310899E+04, 1.330447E+04, 1.350145E+04, 1.369995E+04, 1.389995E+04, 1.410146E+04,
1.430448E+04, 1.450899E+04, 1.471501E+04, 1.492253E+04, 1.513156E+04, 1.534207E+04,
1.555409E+04, 1.576760E+04, 1.598261E+04, 1.619910E+04, 1.641709E+04, 1.663656E+04,
1.685752E+04, 1.707996E+04, 1.730388E+04, 1.752928E+04, 1.775616E+04, 1.798452E+04,
1.821434E+04, 1.844564E+04, 1.867840E+04, 1.891262E+04, 1.914831E+04, 1.938545E+04,
1.962405E+04, 1.986410E+04, 2.010561E+04, 2.034856E+04, 2.059295E+04, 2.083878E+04,
2.108605E+04, 2.133476E+04, 2.158489E+04, 2.183646E+04, 2.208945E+04, 2.234386E+04,
2.259968E+04, 2.285692E+04, 2.311558E+04, 2.337563E+04, 2.363710E+04, 2.389996E+04,
2.416422E+04, 2.442987E+04, 2.469691E+04, 2.496533E+04, 2.523514E+04, 2.550632E+04,
2.577888E+04, 2.605280E+04, 2.632810E+04, 2.660475E+04, 2.688276E+04, 2.716213E+04,
2.744285E+04, 2.772491E+04, 2.800832E+04, 2.829306E+04, 2.857914E+04, 2.886655E+04,
2.915528E+04, 2.944534E+04, 2.973671E+04, 3.002940E+04, 3.032340E+04, 3.061870E+04,
3.091531E+04, 3.121321E+04, 3.151240E+04, 3.181289E+04, 3.211465E+04, 3.241770E+04,
3.272203E+04, 3.302762E+04, 3.333449E+04, 3.364262E+04, 3.395200E+04, 3.426265E+04,
3.457454E+04, 3.488768E+04, 3.520206E+04, 3.551768E+04, 3.583454E+04, 3.615262E+04,
3.647193E+04, 3.679246E+04, 3.711421E+04, 3.743717E+04, 3.776134E+04, 3.808672E+04,
3.841329E+04, 3.874106E+04, 3.907002E+04, 3.940017E+04, 3.973150E+04, 4.006401E+04,
4.039770E+04, 4.073255E+04, 4.106857E+04, 4.140576E+04, 4.174410E+04, 4.208359E+04,
4.242424E+04, 4.276602E+04, 4.310895E+04, 4.345302E+04, 4.379822E+04, 4.414455E+04,
4.449200E+04, 4.484057E+04, 4.519026E+04, 4.554105E+04, 4.589296E+04, 4.624597E+04,
4.660008E+04, 4.695528E+04, 4.731158E+04, 4.766896E+04, 4.802742E+04, 4.838697E+04,
4.874758E+04, 4.910927E+04, 4.947202E+04, 4.983584E+04, 5.020071E+04, 5.056664E+04,
5.093362E+04, 5.130164E+04, 5.167071E+04, 5.204081E+04, 5.241195E+04, 5.278412E+04,
5.315731E+04, 5.353153E+04, 5.390676E+04, 5.428300E+04, 5.466026E+04, 5.503852E+04,
5.541778E+04, 5.579805E+04, 5.617930E+04, 5.656155E+04, 5.694478E+04, 5.732899E+04,
5.771418E+04, 5.810034E+04, 5.848748E+04, 5.887558E+04, 5.926464E+04, 5.965467E+04,
6.004564E+04, 6.043757E+04, 6.083045E+04, 6.122426E+04, 6.161902E+04, 6.201471E+04,
6.241133E+04, 6.280888E+04, 6.320736E+04, 6.360675E+04, 6.400706E+04, 6.440828E+04,
6.481042E+04, 6.521345E+04, 6.561739E+04, 6.602222E+04, 6.642795E+04, 6.683456E+04,
6.724206E+04, 6.765045E+04, 6.805971E+04, 6.846985E+04, 6.888085E+04, 6.929273E+04,
6.970546E+04, 7.011906E+04, 7.053351E+04, 7.094882E+04, 7.136497E+04, 7.178197E+04,
7.219981E+04, 7.261848E+04, 7.303799E+04, 7.345833E+04, 7.387950E+04, 7.430148E+04,
7.472429E+04,
])
# ---------------------- M = 18, I = 1 ---------------------------
M = 18
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
9.761590E+00, 1.797541E+02, 3.589432E+02, 5.384749E+02, 7.198461E+02, 9.059401E+02,
1.099605E+03, 1.302867E+03, 1.516834E+03, 1.742050E+03, 1.978735E+03, 2.226901E+03,
2.486580E+03, 2.757704E+03, 3.040307E+03, 3.334331E+03, 3.639872E+03, 3.956877E+03,
4.285427E+03, 4.625509E+03, 4.977243E+03, 5.340628E+03, 5.715693E+03, 6.102457E+03,
6.501039E+03, 6.911435E+03, 7.333681E+03, 7.767794E+03, 8.213845E+03, 8.671895E+03,
9.141829E+03, 9.623827E+03, 1.011782E+04, 1.062389E+04, 1.114211E+04, 1.167236E+04,
1.221467E+04, 1.276928E+04, 1.333592E+04, 1.391481E+04, 1.450597E+04, 1.510916E+04,
1.572473E+04, 1.635254E+04, 1.699269E+04, 1.764503E+04, 1.830963E+04, 1.898657E+04,
1.967596E+04, 2.037757E+04, 2.109164E+04, 2.181793E+04, 2.255682E+04, 2.330792E+04,
2.407159E+04, 2.484758E+04, 2.563595E+04, 2.643692E+04, 2.725021E+04, 2.807621E+04,
2.891445E+04, 2.976534E+04, 3.062873E+04, 3.150468E+04, 3.239304E+04, 3.329404E+04,
3.420753E+04, 3.513376E+04, 3.607234E+04, 3.702375E+04, 3.798758E+04, 3.896408E+04,
3.995331E+04, 4.095507E+04, 4.196962E+04, 4.299675E+04, 4.403651E+04, 4.508917E+04,
4.615426E+04, 4.723232E+04, 4.832287E+04, 4.942645E+04, 5.054257E+04, 5.167152E+04,
5.281306E+04, 5.396749E+04, 5.513482E+04, 5.631483E+04, 5.750780E+04, 5.871349E+04,
5.993189E+04, 6.116335E+04, 6.240759E+04, 6.366463E+04, 6.493478E+04, 6.621748E+04,
6.751335E+04, 6.882212E+04, 7.014378E+04, 7.147838E+04, 7.282593E+04, 7.418643E+04,
7.555994E+04, 7.694646E+04, 7.834567E+04, 7.975826E+04, 8.118359E+04, 8.262236E+04,
8.407389E+04, 8.553818E+04, 8.701598E+04, 8.850660E+04, 9.001039E+04, 9.152739E+04,
9.305725E+04, 9.460073E+04, 9.615671E+04, 9.772636E+04, 9.930890E+04, 1.009044E+05,
1.025132E+05, 1.041354E+05, 1.057705E+05, 1.074191E+05, 1.090806E+05, 1.107556E+05,
1.124435E+05, 1.141450E+05, 1.158599E+05, 1.175875E+05, 1.193290E+05, 1.210832E+05,
1.228509E+05, 1.246323E+05, 1.264267E+05, 1.282343E+05, 1.300555E+05, 1.318899E+05,
1.337380E+05, 1.355993E+05, 1.374737E+05, 1.393619E+05, 1.412634E+05, 1.431781E+05,
1.451065E+05, 1.470483E+05, 1.490034E+05, 1.509722E+05, 1.529544E+05, 1.549505E+05,
1.569593E+05, 1.589821E+05, 1.610182E+05, 1.630683E+05, 1.651312E+05, 1.672086E+05,
1.692989E+05, 1.714027E+05, 1.735204E+05, 1.756516E+05, 1.777969E+05, 1.799551E+05,
1.821273E+05, 1.843131E+05, 1.865124E+05, 1.887258E+05, 1.909522E+05, 1.931928E+05,
1.954469E+05, 1.977152E+05, 1.999966E+05, 2.022921E+05, 2.046013E+05, 2.069242E+05,
2.092612E+05, 2.116114E+05,
])
# ---------------------- M = 18, I = 2 ---------------------------
M = 18
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
9.912400E+00, 1.828410E+02, 3.651190E+02, 5.477455E+02, 7.322431E+02, 9.215448E+02,
1.118548E+03, 1.325314E+03, 1.543000E+03, 1.772137E+03, 2.012972E+03, 2.265546E+03,
2.529857E+03, 2.805894E+03, 3.093647E+03, 3.393126E+03, 3.704301E+03, 4.027259E+03,
4.362060E+03, 4.708654E+03, 5.067118E+03, 5.437544E+03, 5.819917E+03, 6.214315E+03,
6.620748E+03, 7.039331E+03, 7.469975E+03, 7.912762E+03, 8.367835E+03, 8.835036E+03,
9.314552E+03, 9.806333E+03, 1.031039E+04, 1.082689E+04, 1.135564E+04, 1.189679E+04,
1.245038E+04, 1.301635E+04, 1.359485E+04, 1.418577E+04, 1.478913E+04, 1.540504E+04,
1.603337E+04, 1.667435E+04, 1.732783E+04, 1.799389E+04, 1.867251E+04, 1.936376E+04,
2.006759E+04, 2.078409E+04, 2.151317E+04, 2.225490E+04, 2.300937E+04, 2.377649E+04,
2.455631E+04, 2.534873E+04, 2.615398E+04, 2.697195E+04, 2.780269E+04, 2.864608E+04,
2.950235E+04, 3.037136E+04, 3.125337E+04, 3.214803E+04, 3.305538E+04, 3.397565E+04,
3.490891E+04, 3.585478E+04, 3.681371E+04, 3.778554E+04, 3.877029E+04, 3.976780E+04,
4.077831E+04, 4.180164E+04, 4.283805E+04, 4.388733E+04, 4.494977E+04, 4.602491E+04,
4.711326E+04, 4.821437E+04, 4.932877E+04, 5.045597E+04, 5.159626E+04, 5.274966E+04,
5.391623E+04, 5.509570E+04, 5.628837E+04, 5.749399E+04, 5.871259E+04, 5.994477E+04,
6.118969E+04, 6.244793E+04, 6.371925E+04, 6.500366E+04, 6.630117E+04, 6.761214E+04,
6.893627E+04, 7.027357E+04, 7.162408E+04, 7.298748E+04, 7.436445E+04, 7.575468E+04,
7.715820E+04, 7.857503E+04, 8.000484E+04, 8.144834E+04, 8.290486E+04, 8.437476E+04,
8.585806E+04, 8.735480E+04, 8.886462E+04, 9.038828E+04, 9.192505E+04, 9.347494E+04,
9.503836E+04, 9.661531E+04, 9.820585E+04, 9.980955E+04, 1.014269E+05, 1.030574E+05,
1.047016E+05, 1.063594E+05, 1.080305E+05, 1.097153E+05, 1.114134E+05, 1.131248E+05,
1.148503E+05, 1.165888E+05, 1.183415E+05, 1.201071E+05, 1.218866E+05, 1.236798E+05,
1.254865E+05, 1.273067E+05, 1.291407E+05, 1.309881E+05, 1.328495E+05, 1.347244E+05,
1.366128E+05, 1.385151E+05, 1.404309E+05, 1.423608E+05, 1.443038E+05, 1.462613E+05,
1.482318E+05, 1.502165E+05, 1.522147E+05, 1.542271E+05, 1.562526E+05, 1.582928E+05,
1.603461E+05, 1.624136E+05, 1.644948E+05, 1.665896E+05, 1.686987E+05, 1.708211E+05,
1.729576E+05, 1.751085E+05, 1.772726E+05, 1.794510E+05, 1.816432E+05, 1.838497E+05,
1.860695E+05, 1.883038E+05, 1.905518E+05, 1.928137E+05, 1.950900E+05, 1.973803E+05,
1.996838E+05, 2.020024E+05, 2.043343E+05, 2.066802E+05, 2.090406E+05, 2.114150E+05,
2.138033E+05, 2.162063E+05,
])
# ---------------------- M = 19, I = 1 ---------------------------
M = 19
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
3.780860E+00, 6.888728E+01, 1.374712E+02, 2.060890E+02, 2.747808E+02, 3.438010E+02,
4.137509E+02, 4.855483E+02, 5.602403E+02, 6.388943E+02, 7.225221E+02, 8.120553E+02,
9.083745E+02, 1.012293E+03, 1.124615E+03, 1.246077E+03, 1.377438E+03, 1.519448E+03,
1.672847E+03, 1.838414E+03, 2.016938E+03, 2.209189E+03, 2.416025E+03, 2.638267E+03,
2.876824E+03, 3.132602E+03, 3.406543E+03, 3.699623E+03, 4.012839E+03, 4.347286E+03,
4.703992E+03, 5.084126E+03, 5.488848E+03, 5.919326E+03, 6.376821E+03, 6.862632E+03,
7.378065E+03, 7.924484E+03, 8.503314E+03, 9.115988E+03, 9.764033E+03, 1.044898E+04,
1.117241E+04, 1.193599E+04, 1.274135E+04, 1.359026E+04, 1.448448E+04, 1.542590E+04,
1.641632E+04, 1.745769E+04, 1.855200E+04, 1.970131E+04, 2.090764E+04, 2.217321E+04,
2.350017E+04, 2.489078E+04, 2.634733E+04, 2.787217E+04, 2.946776E+04, 3.113658E+04,
3.288109E+04, 3.470398E+04, 3.660778E+04, 3.859534E+04, 4.066933E+04, 4.283266E+04,
4.508820E+04, 4.743892E+04, 4.988780E+04, 5.243797E+04, 5.509261E+04, 5.785493E+04,
6.072821E+04, 6.371578E+04, 6.682109E+04, 7.004764E+04, 7.339906E+04, 7.687883E+04,
8.049086E+04, 8.423872E+04, 8.812646E+04, 9.215786E+04, 9.633693E+04, 1.006678E+05,
1.051547E+05, 1.098017E+05, 1.146132E+05, 1.195935E+05, 1.247472E+05, 1.300787E+05,
1.355927E+05, 1.412939E+05, 1.471870E+05, 1.532770E+05, 1.595687E+05, 1.660673E+05,
1.727778E+05, 1.797055E+05, 1.868555E+05, 1.942334E+05, 2.018446E+05, 2.096945E+05,
2.177890E+05, 2.261336E+05, 2.347342E+05, 2.435967E+05, 2.527273E+05, 2.621318E+05,
2.718165E+05, 2.817878E+05, 2.920519E+05, 3.026154E+05, 3.134849E+05, 3.246670E+05,
3.361684E+05, 3.479962E+05, 3.601571E+05, 3.726584E+05, 3.855070E+05, 3.987105E+05,
4.122760E+05, 4.262112E+05, 4.405236E+05, 4.552207E+05, 4.703105E+05, 4.858008E+05,
5.016998E+05, 5.180154E+05, 5.347559E+05, 5.519297E+05, 5.695451E+05, 5.876108E+05,
6.061353E+05, 6.251276E+05, 6.445965E+05, 6.645510E+05, 6.850001E+05, 7.059533E+05,
7.274196E+05, 7.494089E+05, 7.719305E+05, 7.949943E+05, 8.186099E+05, 8.427874E+05,
8.675368E+05, 8.928685E+05, 9.187926E+05, 9.453196E+05, 9.724601E+05, 1.000225E+06,
1.028624E+06, 1.057670E+06, 1.087373E+06, 1.117744E+06, 1.148794E+06, 1.180535E+06,
1.212979E+06, 1.246138E+06, 1.280022E+06, 1.314645E+06, 1.350018E+06, 1.386153E+06,
1.423064E+06, 1.460762E+06, 1.499260E+06, 1.538571E+06, 1.578708E+06, 1.619685E+06,
1.661513E+06, 1.704207E+06, 1.747780E+06, 1.792246E+06, 1.837618E+06, 1.883911E+06,
1.931138E+06, 1.979315E+06,
])
# ---------------------- M = 19, I = 2 ---------------------------
M = 19
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
3.866200E+00, 7.060513E+01, 1.409078E+02, 2.112453E+02, 2.816616E+02, 3.524124E+02,
4.241291E+02, 4.977537E+02, 5.743694E+02, 6.550831E+02, 7.409406E+02, 8.329051E+02,
9.318879E+02, 1.038741E+03, 1.154268E+03, 1.279272E+03, 1.414522E+03, 1.560780E+03,
1.718840E+03, 1.889483E+03, 2.073538E+03, 2.271818E+03, 2.485183E+03, 2.714529E+03,
2.960748E+03, 3.224797E+03, 3.507665E+03, 3.810366E+03, 4.133950E+03, 4.479493E+03,
4.848138E+03, 5.241034E+03, 5.659402E+03, 6.104497E+03, 6.577577E+03, 7.080010E+03,
7.613174E+03, 8.178454E+03, 8.777326E+03, 9.411328E+03, 1.008198E+04, 1.079091E+04,
1.153976E+04, 1.233025E+04, 1.316410E+04, 1.404310E+04, 1.496915E+04, 1.594410E+04,
1.696994E+04, 1.804866E+04, 1.918233E+04, 2.037303E+04, 2.162293E+04, 2.293432E+04,
2.430941E+04, 2.575059E+04, 2.726019E+04, 2.884073E+04, 3.049469E+04, 3.222463E+04,
3.403321E+04, 3.592313E+04, 3.789715E+04, 3.995806E+04, 4.210879E+04, 4.435223E+04,
4.669141E+04, 4.912946E+04, 5.166950E+04, 5.431473E+04, 5.706844E+04, 5.993400E+04,
6.291482E+04, 6.601437E+04, 6.923620E+04, 7.258402E+04, 7.606148E+04, 7.967242E+04,
8.342064E+04, 8.731008E+04, 9.134474E+04, 9.552874E+04, 9.986620E+04, 1.043614E+05,
1.090186E+05, 1.138423E+05, 1.188369E+05, 1.240069E+05, 1.293571E+05, 1.348921E+05,
1.406168E+05, 1.465359E+05, 1.526546E+05, 1.589779E+05, 1.655109E+05, 1.722588E+05,
1.792271E+05, 1.864210E+05, 1.938462E+05, 2.015082E+05, 2.094126E+05, 2.175653E+05,
2.259721E+05, 2.346390E+05, 2.435721E+05, 2.527774E+05, 2.622614E+05, 2.720302E+05,
2.820903E+05, 2.924483E+05, 3.031110E+05, 3.140847E+05, 3.253767E+05, 3.369937E+05,
3.489427E+05, 3.612310E+05, 3.738659E+05, 3.868545E+05, 4.002045E+05, 4.139232E+05,
4.280186E+05, 4.424983E+05, 4.573703E+05, 4.726424E+05, 4.883229E+05, 5.044200E+05,
5.209419E+05, 5.378972E+05, 5.552945E+05, 5.731422E+05, 5.914493E+05, 6.102247E+05,
6.294775E+05, 6.492166E+05, 6.694515E+05, 6.901914E+05, 7.114458E+05, 7.332246E+05,
7.555370E+05, 7.783934E+05, 8.018036E+05, 8.257776E+05, 8.503257E+05, 8.754583E+05,
9.011859E+05, 9.275190E+05, 9.544684E+05, 9.820450E+05, 1.010260E+06, 1.039124E+06,
1.068649E+06, 1.098845E+06, 1.129726E+06, 1.161301E+06, 1.193583E+06, 1.226584E+06,
1.260317E+06, 1.294792E+06, 1.330023E+06, 1.366022E+06, 1.402801E+06, 1.440374E+06,
1.478753E+06, 1.517952E+06, 1.557983E+06, 1.598860E+06, 1.640596E+06, 1.683205E+06,
1.726701E+06, 1.771098E+06, 1.816409E+06, 1.862649E+06, 1.909833E+06, 1.957974E+06,
2.007088E+06, 2.057189E+06,
])
# ---------------------- M = 19, I = 3 ---------------------------
M = 19
I = 3
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
7.583710E+00, 1.382173E+02, 2.758282E+02, 4.135070E+02, 5.513676E+02, 6.900135E+02,
8.308340E+02, 9.758329E+02, 1.127263E+03, 1.287312E+03, 1.458080E+03, 1.641493E+03,
1.839335E+03, 2.053277E+03, 2.284972E+03, 2.535982E+03, 2.807868E+03, 3.102189E+03,
3.420546E+03, 3.764540E+03, 4.135842E+03, 4.536154E+03, 4.967247E+03, 5.430907E+03,
5.929040E+03, 6.463587E+03, 7.036596E+03, 7.650158E+03, 8.306416E+03, 9.007672E+03,
9.756218E+03, 1.055447E+04, 1.140497E+04, 1.231027E+04, 1.327302E+04, 1.429603E+04,
1.538213E+04, 1.653432E+04, 1.775555E+04, 1.904896E+04, 2.041788E+04, 2.186555E+04,
2.339541E+04, 2.501105E+04, 2.671603E+04, 2.851411E+04, 3.040920E+04, 3.240513E+04,
3.450607E+04, 3.671615E+04, 3.903959E+04, 4.148094E+04, 4.404455E+04, 4.673515E+04,
4.955745E+04, 5.251625E+04, 5.561666E+04, 5.886377E+04, 6.226278E+04, 6.581899E+04,
6.953795E+04, 7.342535E+04, 7.748685E+04, 8.172822E+04, 8.615570E+04, 9.077534E+04,
9.559340E+04, 1.006162E+05, 1.058507E+05, 1.113031E+05, 1.169806E+05, 1.228899E+05,
1.290384E+05, 1.354334E+05, 1.420821E+05, 1.489923E+05, 1.561716E+05, 1.636280E+05,
1.713695E+05, 1.794043E+05, 1.877407E+05, 1.963875E+05, 2.053530E+05, 2.146464E+05,
2.242765E+05, 2.342527E+05, 2.445840E+05, 2.552802E+05, 2.663510E+05, 2.778062E+05,
2.896558E+05, 3.019102E+05, 3.145796E+05, 3.276747E+05, 3.412063E+05, 3.551851E+05,
3.696226E+05, 3.845298E+05, 3.999184E+05, 4.158000E+05, 4.321865E+05, 4.490901E+05,
4.665230E+05, 4.844975E+05, 5.030266E+05, 5.221230E+05, 5.417996E+05, 5.620701E+05,
5.829476E+05, 6.044458E+05, 6.265791E+05, 6.493608E+05, 6.728060E+05, 6.969287E+05,
7.217437E+05, 7.472664E+05, 7.735113E+05, 8.004945E+05, 8.282311E+05, 8.567373E+05,
8.860289E+05, 9.161222E+05, 9.470339E+05, 9.787808E+05, 1.011380E+06, 1.044848E+06,
1.079203E+06, 1.114462E+06, 1.150644E+06, 1.187766E+06, 1.225847E+06, 1.264906E+06,
1.304961E+06, 1.346032E+06, 1.388138E+06, 1.431299E+06, 1.475534E+06, 1.520864E+06,
1.567309E+06, 1.614891E+06, 1.663628E+06, 1.713544E+06, 1.764659E+06, 1.816995E+06,
1.870575E+06, 1.925419E+06, 1.981552E+06, 2.038995E+06, 2.097772E+06, 2.157905E+06,
2.219420E+06, 2.282339E+06, 2.346687E+06, 2.412488E+06, 2.479766E+06, 2.548548E+06,
2.618858E+06, 2.690721E+06, 2.764165E+06, 2.839214E+06, 2.915895E+06, 2.994236E+06,
3.074263E+06, 3.156003E+06, 3.239484E+06, 3.324735E+06, 3.411783E+06, 3.500657E+06,
3.591387E+06, 3.683999E+06, 3.778527E+06, 3.874996E+06, 3.973440E+06, 4.073888E+06,
4.176370E+06, 4.280919E+06,
])
# ---------------------- M = 19, I = 4 ---------------------------
M = 19
I = 4
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.529716E+01, 2.790462E+02, 5.568808E+02, 8.348524E+02, 1.113124E+03, 1.392726E+03,
1.676127E+03, 1.967024E+03, 2.269724E+03, 2.588528E+03, 2.927570E+03, 3.290640E+03,
3.681356E+03, 4.103003E+03, 4.558813E+03, 5.051868E+03, 5.585175E+03, 6.161870E+03,
6.784985E+03, 7.457593E+03, 8.182895E+03, 8.964139E+03, 9.804763E+03, 1.070818E+04,
1.167802E+04, 1.271790E+04, 1.383181E+04, 1.502371E+04, 1.629759E+04, 1.765790E+04,
1.910900E+04, 2.065548E+04, 2.230214E+04, 2.405374E+04, 2.591542E+04, 2.789238E+04,
2.999010E+04, 3.221416E+04, 3.457015E+04, 3.706422E+04, 3.970237E+04, 4.249085E+04,
4.543618E+04, 4.854516E+04, 5.182439E+04, 5.528126E+04, 5.892283E+04, 6.275655E+04,
6.679011E+04, 7.103145E+04, 7.548857E+04, 8.016983E+04, 8.508371E+04, 9.023910E+04,
9.564456E+04, 1.013096E+05, 1.072436E+05, 1.134560E+05, 1.199569E+05, 1.267562E+05,
1.338645E+05, 1.412920E+05, 1.490498E+05, 1.571490E+05, 1.656006E+05, 1.744166E+05,
1.836087E+05, 1.931887E+05, 2.031692E+05, 2.135629E+05, 2.243826E+05, 2.356413E+05,
2.473527E+05, 2.595302E+05, 2.721881E+05, 2.853405E+05, 2.990019E+05, 3.131871E+05,
3.279114E+05, 3.431903E+05, 3.590394E+05, 3.754746E+05, 3.925124E+05, 4.101694E+05,
4.284625E+05, 4.474091E+05, 4.670263E+05, 4.873326E+05, 5.083460E+05, 5.300849E+05,
5.525683E+05, 5.758153E+05, 5.998453E+05, 6.246787E+05, 6.503350E+05, 6.768352E+05,
7.042000E+05, 7.324508E+05, 7.616091E+05, 7.916970E+05, 8.227364E+05, 8.547503E+05,
8.877616E+05, 9.217938E+05, 9.568705E+05, 9.930163E+05, 1.030255E+06, 1.068612E+06,
1.108113E+06, 1.148782E+06, 1.190646E+06, 1.233733E+06, 1.278068E+06, 1.323678E+06,
1.370591E+06, 1.418836E+06, 1.468441E+06, 1.519434E+06, 1.571845E+06, 1.625704E+06,
1.681041E+06, 1.737886E+06, 1.796270E+06, 1.856224E+06, 1.917781E+06, 1.980972E+06,
2.045832E+06, 2.112391E+06, 2.180684E+06, 2.250746E+06, 2.322610E+06, 2.396312E+06,
2.471886E+06, 2.549370E+06, 2.628798E+06, 2.710208E+06, 2.793637E+06, 2.879123E+06,
2.966704E+06, 3.056419E+06, 3.148307E+06, 3.242406E+06, 3.338759E+06, 3.437405E+06,
3.538386E+06, 3.641742E+06, 3.747517E+06, 3.855753E+06, 3.966492E+06, 4.079779E+06,
4.195658E+06, 4.314174E+06, 4.435371E+06, 4.559296E+06, 4.685994E+06, 4.815512E+06,
4.947899E+06, 5.083202E+06, 5.221468E+06, 5.362749E+06, 5.507091E+06, 5.654546E+06,
5.805164E+06, 5.958997E+06, 6.116096E+06, 6.276514E+06, 6.440302E+06, 6.607517E+06,
6.778209E+06, 6.952435E+06, 7.130249E+06, 7.311707E+06, 7.496867E+06, 7.685785E+06,
7.878517E+06, 8.075122E+06,
])
# ---------------------- M = 19, I = 5 ---------------------------
M = 19
I = 5
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
4.005750E+00, 7.341299E+01, 1.465248E+02, 2.196725E+02, 2.929080E+02, 3.665343E+02,
4.412543E+02, 5.180980E+02, 5.982340E+02, 6.828353E+02, 7.730027E+02, 8.697668E+02,
9.740684E+02, 1.086823E+03, 1.208879E+03, 1.341069E+03, 1.484232E+03, 1.639180E+03,
1.806745E+03, 1.987801E+03, 2.183188E+03, 2.393832E+03, 2.620622E+03, 2.864546E+03,
3.126558E+03, 3.407697E+03, 3.709043E+03, 4.031658E+03, 4.376687E+03, 4.745342E+03,
5.138807E+03, 5.558356E+03, 6.005318E+03, 6.481015E+03, 6.986846E+03, 7.524300E+03,
8.094828E+03, 8.699968E+03, 9.341331E+03, 1.002056E+04, 1.073933E+04, 1.149937E+04,
1.230251E+04, 1.315057E+04, 1.404547E+04, 1.498914E+04, 1.598358E+04, 1.703092E+04,
1.813322E+04, 1.929266E+04, 2.051152E+04, 2.179207E+04, 2.313669E+04, 2.454777E+04,
2.602783E+04, 2.757939E+04, 2.920503E+04, 3.090744E+04, 3.268941E+04, 3.455366E+04,
3.650309E+04, 3.854064E+04, 4.066930E+04, 4.289217E+04, 4.521234E+04, 4.763307E+04,
5.015761E+04, 5.278935E+04, 5.553171E+04, 5.838817E+04, 6.136237E+04, 6.445786E+04,
6.767842E+04, 7.102788E+04, 7.451010E+04, 7.812908E+04, 8.188878E+04, 8.579338E+04,
8.984714E+04, 9.405423E+04, 9.841911E+04, 1.029462E+05, 1.076400E+05, 1.125052E+05,
1.175464E+05, 1.227686E+05, 1.281765E+05, 1.337751E+05, 1.395696E+05, 1.455650E+05,
1.517667E+05, 1.581798E+05, 1.648099E+05, 1.716626E+05, 1.787433E+05, 1.860579E+05,
1.936120E+05, 2.014118E+05, 2.094632E+05, 2.177721E+05, 2.263450E+05, 2.351881E+05,
2.443078E+05, 2.537105E+05, 2.634030E+05, 2.733918E+05, 2.836841E+05, 2.942864E+05,
3.052060E+05, 3.164500E+05, 3.280255E+05, 3.399401E+05, 3.522011E+05, 3.648163E+05,
3.777931E+05, 3.911396E+05, 4.048633E+05, 4.189728E+05, 4.334758E+05, 4.483808E+05,
4.636960E+05, 4.794302E+05, 4.955917E+05, 5.121894E+05, 5.292323E+05, 5.467291E+05,
5.646892E+05, 5.831215E+05, 6.020357E+05, 6.214410E+05, 6.413472E+05, 6.617640E+05,
6.827011E+05, 7.041687E+05, 7.261769E+05, 7.487358E+05, 7.718559E+05, 7.955477E+05,
8.198219E+05, 8.446893E+05, 8.701605E+05, 8.962469E+05, 9.229596E+05, 9.503098E+05,
9.783092E+05, 1.006969E+06, 1.036302E+06, 1.066319E+06, 1.097032E+06, 1.128454E+06,
1.160596E+06, 1.193472E+06, 1.227094E+06, 1.261474E+06, 1.296626E+06, 1.332563E+06,
1.369297E+06, 1.406843E+06, 1.445213E+06, 1.484422E+06, 1.524483E+06, 1.565410E+06,
1.607217E+06, 1.649919E+06, 1.693530E+06, 1.738064E+06, 1.783536E+06, 1.829962E+06,
1.877355E+06, 1.925733E+06, 1.975109E+06, 2.025500E+06, 2.076920E+06, 2.129387E+06,
2.182916E+06, 2.237523E+06,
])
# ---------------------- M = 20, I = 1 ---------------------------
M = 20
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.091110E+00, 4.947081E+01, 1.416480E+02, 2.592507E+02, 3.983023E+02, 5.559709E+02,
7.302920E+02, 9.198257E+02, 1.123477E+03, 1.340418E+03, 1.570058E+03, 1.812038E+03,
2.066230E+03, 2.332728E+03, 2.611836E+03, 2.904051E+03, 3.210045E+03, 3.530639E+03,
3.866789E+03, 4.219566E+03, 4.590146E+03, 4.979793E+03, 5.389860E+03, 5.821776E+03,
6.277046E+03, 6.757251E+03, 7.264045E+03, 7.799159E+03, 8.364401E+03, 8.961659E+03,
9.592904E+03, 1.026019E+04, 1.096512E+04, 1.170966E+04, 1.249659E+04, 1.332840E+04,
1.420757E+04, 1.513680E+04, 1.611862E+04, 1.715616E+04, 1.825218E+04, 1.940974E+04,
2.063218E+04, 2.192282E+04, 2.328522E+04, 2.472293E+04, 2.623979E+04, 2.783973E+04,
2.952702E+04, 3.130573E+04, 3.318041E+04, 3.515575E+04, 3.723663E+04, 3.942790E+04,
4.173473E+04, 4.416289E+04, 4.671773E+04, 4.940519E+04, 5.223118E+04, 5.520223E+04,
5.832512E+04, 6.160614E+04, 6.505245E+04, 6.867173E+04, 7.247112E+04, 7.645903E+04,
8.064278E+04, 8.503157E+04, 8.963402E+04, 9.445875E+04, 9.951574E+04, 1.048142E+05,
1.103643E+05, 1.161765E+05, 1.222616E+05, 1.286309E+05, 1.352955E+05, 1.422674E+05,
1.495587E+05, 1.571827E+05, 1.651519E+05, 1.734802E+05, 1.821813E+05, 1.912699E+05,
2.007604E+05, 2.106690E+05, 2.210108E+05, 2.318027E+05, 2.430609E+05, 2.548036E+05,
2.670483E+05, 2.798136E+05, 2.931184E+05, 3.069823E+05, 3.214256E+05, 3.364693E+05,
3.521348E+05, 3.684439E+05, 3.854199E+05, 4.030855E+05, 4.214650E+05, 4.405834E+05,
4.604660E+05, 4.811396E+05, 5.026303E+05, 5.249660E+05, 5.481756E+05, 5.722878E+05,
5.973333E+05, 6.233430E+05, 6.503482E+05, 6.783817E+05, 7.074775E+05, 7.376698E+05,
7.689940E+05, 8.014861E+05, 8.351831E+05, 8.701245E+05, 9.063479E+05, 9.438955E+05,
9.828070E+05, 1.023125E+06, 1.064895E+06, 1.108158E+06, 1.152963E+06, 1.199355E+06,
1.247383E+06, 1.297095E+06, 1.348543E+06, 1.401778E+06, 1.456853E+06, 1.513823E+06,
1.572742E+06, 1.633669E+06, 1.696660E+06, 1.761777E+06, 1.829081E+06, 1.898632E+06,
1.970497E+06, 2.044741E+06, 2.121430E+06, 2.200634E+06, 2.282422E+06, 2.366867E+06,
2.454042E+06, 2.544023E+06, 2.636886E+06, 2.732710E+06, 2.831576E+06, 2.933566E+06,
3.038763E+06, 3.147255E+06, 3.259128E+06, 3.374473E+06, 3.493381E+06, 3.615946E+06,
3.742264E+06, 3.872434E+06, 4.006554E+06, 4.144726E+06, 4.287056E+06, 4.433650E+06,
4.584616E+06, 4.740066E+06, 4.900110E+06, 5.064870E+06, 5.234458E+06, 5.408999E+06,
5.588613E+06, 5.773427E+06, 5.963569E+06, 6.159169E+06, 6.360361E+06, 6.567283E+06,
6.780068E+06, 6.998864E+06,
])
# ---------------------- M = 20, I = 2 ---------------------------
M = 20
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.519830E+00, 1.041424E+02, 2.906766E+02, 5.317342E+02, 8.170004E+02, 1.140498E+03,
1.498191E+03, 1.887134E+03, 2.305068E+03, 2.750313E+03, 3.221668E+03, 3.718356E+03,
4.240095E+03, 4.787152E+03, 5.360055E+03, 5.959825E+03, 6.587858E+03, 7.245886E+03,
7.935732E+03, 8.659593E+03, 9.420003E+03, 1.021942E+04, 1.106064E+04, 1.194646E+04,
1.288013E+04, 1.386475E+04, 1.490370E+04, 1.600056E+04, 1.715893E+04, 1.838256E+04,
1.967551E+04, 2.104202E+04, 2.248633E+04, 2.401319E+04, 2.562698E+04, 2.733280E+04,
2.913575E+04, 3.104133E+04, 3.305479E+04, 3.518252E+04, 3.743016E+04, 3.980401E+04,
4.231092E+04, 4.495767E+04, 4.775159E+04, 5.069997E+04, 5.381064E+04, 5.709169E+04,
6.055190E+04, 6.419956E+04, 6.804403E+04, 7.209494E+04, 7.636229E+04, 8.085600E+04,
8.558671E+04, 9.056622E+04, 9.580554E+04, 1.013168E+05, 1.071122E+05, 1.132050E+05,
1.196093E+05, 1.263378E+05, 1.334053E+05, 1.408275E+05, 1.486191E+05, 1.567972E+05,
1.653770E+05, 1.743773E+05, 1.838157E+05, 1.937100E+05, 2.040806E+05, 2.149463E+05,
2.263282E+05, 2.382475E+05, 2.507265E+05, 2.637882E+05, 2.774557E+05, 2.917531E+05,
3.067057E+05, 3.223406E+05, 3.386834E+05, 3.557626E+05, 3.736064E+05, 3.922448E+05,
4.117074E+05, 4.320273E+05, 4.532358E+05, 4.753672E+05, 4.984549E+05, 5.225361E+05,
5.476468E+05, 5.738251E+05, 6.011099E+05, 6.295411E+05, 6.591607E+05, 6.900115E+05,
7.221373E+05, 7.555835E+05, 7.903966E+05, 8.266243E+05, 8.643161E+05, 9.035230E+05,
9.442971E+05, 9.866935E+05, 1.030765E+06, 1.076570E+06, 1.124167E+06, 1.173615E+06,
1.224977E+06, 1.278317E+06, 1.333697E+06, 1.391187E+06, 1.450855E+06, 1.512772E+06,
1.577010E+06, 1.643643E+06, 1.712747E+06, 1.784403E+06, 1.858688E+06, 1.935688E+06,
2.015486E+06, 2.098169E+06, 2.183827E+06, 2.272550E+06, 2.364432E+06, 2.459570E+06,
2.558063E+06, 2.660012E+06, 2.765517E+06, 2.874690E+06, 2.987634E+06, 3.104464E+06,
3.225293E+06, 3.350239E+06, 3.479418E+06, 3.612957E+06, 3.750979E+06, 3.893612E+06,
4.040990E+06, 4.193245E+06, 4.350513E+06, 4.512941E+06, 4.680667E+06, 4.853844E+06,
5.032618E+06, 5.217145E+06, 5.407584E+06, 5.604095E+06, 5.806845E+06, 6.016000E+06,
6.231733E+06, 6.454222E+06, 6.683645E+06, 6.920189E+06, 7.164040E+06, 7.415389E+06,
7.674437E+06, 7.941381E+06, 8.216427E+06, 8.499785E+06, 8.791667E+06, 9.092294E+06,
9.401888E+06, 9.720676E+06, 1.004889E+07, 1.038677E+07, 1.073455E+07, 1.109249E+07,
1.146083E+07, 1.183984E+07, 1.222977E+07, 1.263090E+07, 1.304350E+07, 1.346784E+07,
1.390421E+07, 1.435290E+07,
])
# ---------------------- M = 20, I = 3 ---------------------------
M = 20
I = 3
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
7.773000E-01, 5.324867E+01, 1.486677E+02, 2.719834E+02, 4.179176E+02, 5.834117E+02,
7.664008E+02, 9.653781E+02, 1.179188E+03, 1.406969E+03, 1.648109E+03, 1.902210E+03,
2.169127E+03, 2.448996E+03, 2.742088E+03, 3.048926E+03, 3.370223E+03, 3.706865E+03,
4.059785E+03, 4.430108E+03, 4.819128E+03, 5.228105E+03, 5.658465E+03, 6.111648E+03,
6.589308E+03, 7.093032E+03, 7.624553E+03, 8.185700E+03, 8.778316E+03, 9.404317E+03,
1.006578E+04, 1.076488E+04, 1.150378E+04, 1.228491E+04, 1.311052E+04, 1.398321E+04,
1.490558E+04, 1.588047E+04, 1.691055E+04, 1.799908E+04, 1.914896E+04, 2.036341E+04,
2.164594E+04, 2.300000E+04, 2.442935E+04, 2.593773E+04, 2.752913E+04, 2.920771E+04,
3.097792E+04, 3.284406E+04, 3.481086E+04, 3.688330E+04, 3.906645E+04, 4.136541E+04,
4.378561E+04, 4.633311E+04, 4.901353E+04, 5.183307E+04, 5.479795E+04, 5.791503E+04,
6.119141E+04, 6.463369E+04, 6.824939E+04, 7.204656E+04, 7.603270E+04, 8.021661E+04,
8.460600E+04, 8.921050E+04, 9.403916E+04, 9.910103E+04, 1.044066E+05, 1.099654E+05,
1.157884E+05, 1.218862E+05, 1.282704E+05, 1.349528E+05, 1.419450E+05, 1.492595E+05,
1.569092E+05, 1.649080E+05, 1.732688E+05, 1.820065E+05, 1.911353E+05, 2.006707E+05,
2.106276E+05, 2.210232E+05, 2.318735E+05, 2.431958E+05, 2.550074E+05, 2.673273E+05,
2.801738E+05, 2.935665E+05, 3.075254E+05, 3.220707E+05, 3.372240E+05, 3.530071E+05,
3.694426E+05, 3.865535E+05, 4.043638E+05, 4.228977E+05, 4.421807E+05, 4.622389E+05,
4.830988E+05, 5.047886E+05, 5.273357E+05, 5.507693E+05, 5.751197E+05, 6.004172E+05,
6.266939E+05, 6.539820E+05, 6.823147E+05, 7.117262E+05, 7.422522E+05, 7.739285E+05,
8.067927E+05, 8.408819E+05, 8.762352E+05, 9.128942E+05, 9.508983E+05, 9.902916E+05,
1.031116E+06, 1.073416E+06, 1.117238E+06, 1.162629E+06, 1.209636E+06, 1.258308E+06,
1.308697E+06, 1.360853E+06, 1.414829E+06, 1.470682E+06, 1.528464E+06, 1.588234E+06,
1.650050E+06, 1.713972E+06, 1.780059E+06, 1.848378E+06, 1.918989E+06, 1.991960E+06,
2.067358E+06, 2.145251E+06, 2.225709E+06, 2.308807E+06, 2.394615E+06, 2.483212E+06,
2.574672E+06, 2.669076E+06, 2.766504E+06, 2.867038E+06, 2.970764E+06, 3.077768E+06,
3.188136E+06, 3.301961E+06, 3.419333E+06, 3.540348E+06, 3.665102E+06, 3.793692E+06,
3.926220E+06, 4.062788E+06, 4.203501E+06, 4.348466E+06, 4.497792E+06, 4.651592E+06,
4.809980E+06, 4.973071E+06, 5.140983E+06, 5.313842E+06, 5.491767E+06, 5.674888E+06,
5.863331E+06, 6.057231E+06, 6.256719E+06, 6.461935E+06, 6.673017E+06, 6.890111E+06,
7.113357E+06, 7.342907E+06,
])
# ---------------------- M = 21, I = 1 ---------------------------
M = 21
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
3.981460E+00, 3.304167E+02, 9.292313E+02, 1.704088E+03, 2.621559E+03, 3.662421E+03,
4.814121E+03, 6.068541E+03, 7.421071E+03, 8.869781E+03, 1.041512E+04, 1.205911E+04,
1.380519E+04, 1.565730E+04, 1.762056E+04, 1.970014E+04, 2.190181E+04, 2.423163E+04,
2.669554E+04, 2.929991E+04, 3.205132E+04, 3.495586E+04, 3.802056E+04, 4.125226E+04,
4.465708E+04, 4.824259E+04, 5.201564E+04, 5.598309E+04, 6.015219E+04, 6.453072E+04,
6.912550E+04, 7.394438E+04, 7.899525E+04, 8.428610E+04, 8.982435E+04, 9.561869E+04,
1.016773E+05, 1.080083E+05, 1.146215E+05, 1.215237E+05, 1.287266E+05, 1.362371E+05,
1.440654E+05, 1.522203E+05, 1.607132E+05, 1.695523E+05, 1.787484E+05, 1.883127E+05,
1.982535E+05, 2.085833E+05, 2.193115E+05, 2.304511E+05, 2.420107E+05, 2.540037E+05,
2.664413E+05, 2.793348E+05, 2.926958E+05, 3.065372E+05, 3.208723E+05, 3.357116E+05,
3.510703E+05, 3.669592E+05, 3.833922E+05, 4.003837E+05, 4.179467E+05, 4.360939E+05,
4.548404E+05, 4.741996E+05, 4.941865E+05, 5.148171E+05, 5.361032E+05, 5.580627E+05,
5.807100E+05, 6.040616E+05, 6.281301E+05, 6.529321E+05, 6.784872E+05, 7.048083E+05,
7.319154E+05, 7.598219E+05, 7.885455E+05, 8.181023E+05, 8.485133E+05, 8.797949E+05,
9.119639E+05, 9.450366E+05, 9.790353E+05, 1.013977E+06, 1.049882E+06, 1.086768E+06,
1.124652E+06, 1.163556E+06, 1.203500E+06, 1.244505E+06, 1.286588E+06, 1.329770E+06,
1.374075E+06, 1.419521E+06, 1.466130E+06, 1.513921E+06, 1.562920E+06, 1.613148E+06,
1.664626E+06, 1.717375E+06, 1.771419E+06, 1.826780E+06, 1.883484E+06, 1.941552E+06,
2.001009E+06, 2.061877E+06, 2.124181E+06, 2.187948E+06, 2.253196E+06, 2.319956E+06,
2.388251E+06, 2.458107E+06, 2.529549E+06, 2.602607E+06, 2.677298E+06, 2.753658E+06,
2.831706E+06, 2.911473E+06, 2.992987E+06, 3.076272E+06, 3.161364E+06, 3.248277E+06,
3.337053E+06, 3.427717E+06, 3.520289E+06, 3.614807E+06, 3.711301E+06, 3.809798E+06,
3.910326E+06, 4.012918E+06, 4.117603E+06, 4.224413E+06, 4.333378E+06, 4.444528E+06,
4.557903E+06, 4.673519E+06, 4.791422E+06, 4.911642E+06, 5.034208E+06, 5.159155E+06,
5.286513E+06, 5.416319E+06, 5.548609E+06, 5.683412E+06, 5.820768E+06, 5.960706E+06,
6.103265E+06, 6.248481E+06, 6.396387E+06, 6.547017E+06, 6.700413E+06, 6.856604E+06,
7.015634E+06, 7.177539E+06, 7.342350E+06, 7.510115E+06, 7.680862E+06, 7.854632E+06,
8.031473E+06, 8.211412E+06, 8.394491E+06, 8.580756E+06, 8.770230E+06, 8.962977E+06,
9.159022E+06, 9.358408E+06, 9.561181E+06, 9.767369E+06, 9.977034E+06, 1.019021E+07,
1.040692E+07, 1.062723E+07,
])
# ---------------------- M = 21, I = 2 ---------------------------
M = 21
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
4.058120E+00, 3.362054E+02, 9.456074E+02, 1.734179E+03, 2.667896E+03, 3.727193E+03,
4.899294E+03, 6.175937E+03, 7.552431E+03, 9.026809E+03, 1.059953E+04, 1.227265E+04,
1.404968E+04, 1.593461E+04, 1.793265E+04, 2.004908E+04, 2.228977E+04, 2.466088E+04,
2.716846E+04, 2.981898E+04, 3.261916E+04, 3.557517E+04, 3.869418E+04, 4.198316E+04,
4.544832E+04, 4.909737E+04, 5.293730E+04, 5.697506E+04, 6.121805E+04, 6.567419E+04,
7.035040E+04, 7.525467E+04, 8.039507E+04, 8.577969E+04, 9.141609E+04, 9.731312E+04,
1.034791E+05, 1.099223E+05, 1.166527E+05, 1.236773E+05, 1.310079E+05, 1.386514E+05,
1.466185E+05, 1.549179E+05, 1.635614E+05, 1.725571E+05, 1.819162E+05, 1.916501E+05,
2.017671E+05, 2.122799E+05, 2.231982E+05, 2.345353E+05, 2.462997E+05, 2.585053E+05,
2.711634E+05, 2.842854E+05, 2.978833E+05, 3.119700E+05, 3.265592E+05, 3.416615E+05,
3.572924E+05, 3.734629E+05, 3.901872E+05, 4.074799E+05, 4.253541E+05, 4.438230E+05,
4.629018E+05, 4.826041E+05, 5.029453E+05, 5.239416E+05, 5.456049E+05, 5.679538E+05,
5.910025E+05, 6.147679E+05, 6.392630E+05, 6.645047E+05, 6.905128E+05, 7.173004E+05,
7.448879E+05, 7.732890E+05, 8.025217E+05, 8.326025E+05, 8.635525E+05, 8.953886E+05,
9.281278E+05, 9.617867E+05, 9.963880E+05, 1.031949E+06, 1.068491E+06, 1.106030E+06,
1.144586E+06, 1.184179E+06, 1.224831E+06, 1.266563E+06, 1.309392E+06, 1.353339E+06,
1.398430E+06, 1.444682E+06, 1.492116E+06, 1.540754E+06, 1.590622E+06, 1.641741E+06,
1.694130E+06, 1.747816E+06, 1.802817E+06, 1.859159E+06, 1.916869E+06, 1.975965E+06,
2.036476E+06, 2.098423E+06, 2.161832E+06, 2.226729E+06, 2.293134E+06, 2.361077E+06,
2.430583E+06, 2.501677E+06, 2.574385E+06, 2.648739E+06, 2.724753E+06, 2.802466E+06,
2.881898E+06, 2.963079E+06, 3.046038E+06, 3.130799E+06, 3.217399E+06, 3.305853E+06,
3.396203E+06, 3.488473E+06, 3.582687E+06, 3.678880E+06, 3.777084E+06, 3.877327E+06,
3.979638E+06, 4.084047E+06, 4.190589E+06, 4.299291E+06, 4.410188E+06, 4.523309E+06,
4.638693E+06, 4.756358E+06, 4.876351E+06, 4.998702E+06, 5.123440E+06, 5.250602E+06,
5.380218E+06, 5.512325E+06, 5.646960E+06, 5.784152E+06, 5.923943E+06, 6.066361E+06,
6.211446E+06, 6.359237E+06, 6.509766E+06, 6.663065E+06, 6.819180E+06, 6.978140E+06,
7.139989E+06, 7.304764E+06, 7.472497E+06, 7.643236E+06, 7.817009E+06, 7.993858E+06,
8.173833E+06, 8.356963E+06, 8.543287E+06, 8.732853E+06, 8.925687E+06, 9.121849E+06,
9.321369E+06, 9.524290E+06, 9.730657E+06, 9.940501E+06, 1.015388E+07, 1.037083E+07,
1.059139E+07, 1.081561E+07,
])
# ---------------------- M = 22, I = 1 ---------------------------
M = 22
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[3]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
6.029370E+00, 3.298562E+01, 6.440722E+01, 9.584644E+01, 1.272919E+02, 1.587414E+02,
1.901941E+02, 2.216498E+02, 2.531083E+02, 2.845695E+02, 3.160334E+02, 3.475000E+02,
3.789694E+02, 4.104418E+02, 4.419180E+02, 4.733988E+02, 5.048860E+02, 5.363820E+02,
5.678901E+02, 5.994148E+02, 6.309613E+02, 6.625362E+02, 6.941467E+02, 7.258012E+02,
7.575086E+02, 7.892788E+02, 8.211221E+02, 8.530492E+02, 8.850710E+02, 9.171988E+02,
9.494439E+02, 9.818175E+02, 1.014331E+03, 1.046995E+03, 1.079820E+03, 1.112818E+03,
1.145998E+03, 1.179370E+03, 1.212943E+03, 1.246728E+03, 1.280732E+03, 1.314964E+03,
1.349432E+03, 1.384144E+03, 1.419107E+03, 1.454327E+03, 1.489813E+03, 1.525569E+03,
1.561602E+03, 1.597917E+03, 1.634520E+03, 1.671416E+03, 1.708610E+03, 1.746106E+03,
1.783909E+03, 1.822022E+03, 1.860449E+03, 1.899195E+03, 1.938262E+03, 1.977654E+03,
2.017374E+03, 2.057425E+03, 2.097810E+03, 2.138531E+03, 2.179590E+03, 2.220991E+03,
2.262736E+03, 2.304826E+03, 2.347264E+03, 2.390051E+03, 2.433189E+03, 2.476681E+03,
2.520527E+03, 2.564729E+03, 2.609289E+03, 2.654208E+03, 2.699487E+03, 2.745128E+03,
2.791131E+03, 2.837498E+03, 2.884230E+03, 2.931327E+03, 2.978791E+03, 3.026622E+03,
3.074822E+03, 3.123390E+03, 3.172328E+03, 3.221636E+03, 3.271315E+03, 3.321366E+03,
3.371788E+03, 3.422582E+03, 3.473749E+03, 3.525290E+03, 3.577203E+03, 3.629490E+03,
3.682151E+03, 3.735186E+03, 3.788596E+03, 3.842379E+03, 3.896538E+03, 3.951070E+03,
4.005977E+03, 4.061259E+03, 4.116915E+03, 4.172946E+03, 4.229351E+03, 4.286130E+03,
4.343283E+03, 4.400810E+03, 4.458710E+03, 4.516984E+03, 4.575631E+03, 4.634651E+03,
4.694043E+03, 4.753807E+03, 4.813944E+03, 4.874451E+03, 4.935329E+03, 4.996578E+03,
5.058196E+03, 5.120184E+03, 5.182541E+03, 5.245267E+03, 5.308360E+03, 5.371821E+03,
5.435648E+03, 5.499841E+03, 5.564399E+03, 5.629323E+03, 5.694610E+03, 5.760261E+03,
5.826274E+03, 5.892649E+03, 5.959385E+03, 6.026482E+03, 6.093938E+03, 6.161753E+03,
6.229926E+03, 6.298457E+03, 6.367343E+03, 6.436585E+03, 6.506181E+03, 6.576131E+03,
6.646434E+03, 6.717089E+03, 6.788095E+03, 6.859450E+03, 6.931155E+03, 7.003207E+03,
7.075607E+03, 7.148353E+03, 7.221444E+03, 7.294879E+03, 7.368657E+03, 7.442778E+03,
7.517239E+03, 7.592040E+03, 7.667180E+03, 7.742658E+03, 7.818473E+03, 7.894623E+03,
7.971108E+03, 8.047926E+03, 8.125077E+03, 8.202560E+03, 8.280372E+03, 8.358513E+03,
8.436983E+03, 8.515779E+03, 8.594900E+03, 8.674346E+03, 8.754116E+03, 8.834207E+03,
8.914620E+03, 8.995352E+03, 9.076403E+03, 9.157772E+03, 9.239456E+03, 9.321456E+03,
9.403770E+03, 9.486396E+03, 9.569334E+03, 9.652582E+03, 9.736138E+03, 9.820003E+03,
9.904175E+03, 9.988651E+03, 1.007343E+04, 1.015852E+04, 1.024390E+04, 1.032959E+04,
1.041557E+04, 1.050186E+04, 1.058844E+04, 1.067531E+04, 1.076248E+04, 1.084994E+04,
1.093770E+04, 1.102574E+04, 1.111408E+04, 1.120270E+04, 1.129161E+04, 1.138080E+04,
1.147028E+04, 1.156004E+04, 1.165009E+04, 1.174041E+04, 1.183101E+04, 1.192189E+04,
1.201305E+04, 1.210448E+04, 1.219619E+04, 1.228817E+04, 1.238042E+04, 1.247295E+04,
1.256574E+04, 1.265880E+04, 1.275212E+04, 1.284571E+04, 1.293957E+04, 1.303369E+04,
1.312807E+04, 1.322271E+04, 1.331760E+04, 1.341276E+04, 1.350817E+04, 1.360384E+04,
1.369976E+04, 1.379594E+04, 1.389237E+04, 1.398904E+04, 1.408597E+04, 1.418314E+04,
1.428056E+04, 1.437823E+04, 1.447614E+04, 1.457429E+04, 1.467269E+04, 1.477132E+04,
1.487019E+04, 1.496930E+04, 1.506865E+04, 1.516823E+04, 1.526805E+04, 1.536810E+04,
1.546838E+04, 1.556890E+04, 1.566964E+04, 1.577061E+04, 1.587180E+04, 1.597322E+04,
1.607487E+04, 1.617674E+04, 1.627883E+04, 1.638114E+04, 1.648367E+04, 1.658642E+04,
1.668939E+04, 1.679257E+04, 1.689597E+04, 1.699958E+04, 1.710340E+04, 1.720744E+04,
1.731168E+04, 1.741613E+04, 1.752079E+04, 1.762566E+04, 1.773073E+04, 1.783601E+04,
1.794149E+04, 1.804717E+04, 1.815305E+04, 1.825913E+04, 1.836541E+04, 1.847189E+04,
1.857856E+04, 1.868542E+04, 1.879248E+04, 1.889974E+04, 1.900718E+04, 1.911481E+04,
1.922264E+04, 1.933065E+04, 1.943885E+04, 1.954723E+04, 1.965580E+04, 1.976455E+04,
1.987348E+04, 1.998260E+04, 2.009189E+04, 2.020137E+04, 2.031102E+04, 2.042085E+04,
2.053085E+04, 2.064103E+04, 2.075138E+04, 2.086191E+04, 2.097260E+04, 2.108347E+04,
2.119450E+04, 2.130571E+04, 2.141708E+04, 2.152862E+04, 2.164032E+04, 2.175218E+04,
2.186421E+04, 2.197640E+04, 2.208875E+04, 2.220126E+04, 2.231393E+04, 2.242675E+04,
2.253974E+04, 2.265287E+04, 2.276617E+04, 2.287961E+04, 2.299321E+04, 2.310696E+04,
2.322086E+04, 2.333491E+04, 2.344911E+04, 2.356345E+04, 2.367794E+04, 2.379258E+04,
2.390736E+04, 2.402228E+04, 2.413735E+04, 2.425256E+04, 2.436791E+04, 2.448339E+04,
2.459902E+04, 2.471478E+04, 2.483069E+04, 2.494672E+04, 2.506289E+04, 2.517920E+04,
2.529563E+04, 2.541220E+04, 2.552890E+04, 2.564573E+04, 2.576269E+04, 2.587978E+04,
2.599699E+04, 2.611433E+04, 2.623180E+04, 2.634939E+04, 2.646710E+04, 2.658493E+04,
2.670289E+04, 2.682097E+04, 2.693917E+04, 2.705748E+04, 2.717592E+04, 2.729447E+04,
2.741314E+04, 2.753192E+04, 2.765082E+04, 2.776984E+04, 2.788896E+04, 2.800820E+04,
2.812754E+04, 2.824700E+04, 2.836657E+04, 2.848625E+04, 2.860603E+04, 2.872592E+04,
2.884592E+04, 2.896602E+04, 2.908623E+04, 2.920654E+04, 2.932695E+04, 2.944746E+04,
2.956808E+04, 2.968880E+04, 2.980961E+04, 2.993053E+04, 3.005154E+04, 3.017265E+04,
3.029385E+04, 3.041515E+04, 3.053655E+04, 3.065804E+04, 3.077962E+04, 3.090129E+04,
3.102306E+04, 3.114492E+04, 3.126686E+04, 3.138890E+04, 3.151103E+04, 3.163324E+04,
3.175554E+04, 3.187793E+04, 3.200040E+04, 3.212295E+04, 3.224560E+04, 3.236832E+04,
3.249113E+04, 3.261402E+04, 3.273698E+04, 3.286004E+04, 3.298317E+04, 3.310637E+04,
3.322966E+04, 3.335303E+04, 3.347647E+04, 3.359999E+04, 3.372358E+04, 3.384725E+04,
3.397099E+04, 3.409481E+04, 3.421870E+04, 3.434266E+04, 3.446669E+04, 3.459079E+04,
3.471497E+04, 3.483921E+04, 3.496352E+04, 3.508790E+04, 3.521235E+04, 3.533686E+04,
3.546144E+04, 3.558609E+04, 3.571080E+04, 3.583558E+04, 3.596041E+04, 3.608532E+04,
3.621028E+04, 3.633531E+04, 3.646039E+04, 3.658554E+04, 3.671075E+04, 3.683601E+04,
3.696134E+04, 3.708672E+04, 3.721216E+04, 3.733766E+04, 3.746321E+04, 3.758882E+04,
3.771449E+04, 3.784021E+04, 3.796598E+04, 3.809181E+04, 3.821769E+04, 3.834362E+04,
3.846960E+04, 3.859563E+04, 3.872172E+04, 3.884785E+04, 3.897404E+04, 3.910027E+04,
3.922655E+04,
])
# ---------------------- M = 22, I = 2 ---------------------------
M = 22
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[3]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
6.071020E+00, 4.541678E+01, 8.875137E+01, 1.321089E+02, 1.754746E+02, 2.188458E+02,
2.622213E+02, 3.056010E+02, 3.489845E+02, 3.923717E+02, 4.357626E+02, 4.791572E+02,
5.225558E+02, 5.659587E+02, 6.093669E+02, 6.527822E+02, 6.962070E+02, 7.396453E+02,
7.831022E+02, 8.265842E+02, 8.700997E+02, 9.136581E+02, 9.572704E+02, 1.000949E+03,
1.044707E+03, 1.088558E+03, 1.132518E+03, 1.176603E+03, 1.220827E+03, 1.265207E+03,
1.309760E+03, 1.354500E+03, 1.399444E+03, 1.444608E+03, 1.490006E+03, 1.535654E+03,
1.581565E+03, 1.627754E+03, 1.674233E+03, 1.721016E+03, 1.768115E+03, 1.815541E+03,
1.863305E+03, 1.911418E+03, 1.959890E+03, 2.008730E+03, 2.057948E+03, 2.107552E+03,
2.157550E+03, 2.207950E+03, 2.258760E+03, 2.309985E+03, 2.361634E+03, 2.413711E+03,
2.466223E+03, 2.519175E+03, 2.572573E+03, 2.626421E+03, 2.680724E+03, 2.735487E+03,
2.790713E+03, 2.846407E+03, 2.902573E+03, 2.959214E+03, 3.016333E+03, 3.073933E+03,
3.132018E+03, 3.190590E+03, 3.249653E+03, 3.309208E+03, 3.369258E+03, 3.429805E+03,
3.490852E+03, 3.552400E+03, 3.614451E+03, 3.677007E+03, 3.740070E+03, 3.803642E+03,
3.867723E+03, 3.932316E+03, 3.997421E+03, 4.063040E+03, 4.129174E+03, 4.195823E+03,
4.262990E+03, 4.330675E+03, 4.398878E+03, 4.467600E+03, 4.536843E+03, 4.606606E+03,
4.676891E+03, 4.747698E+03, 4.819026E+03, 4.890878E+03, 4.963252E+03, 5.036150E+03,
5.109571E+03, 5.183515E+03, 5.257983E+03, 5.332975E+03, 5.408491E+03, 5.484530E+03,
5.561093E+03, 5.638179E+03, 5.715789E+03, 5.793921E+03, 5.872577E+03, 5.951754E+03,
6.031454E+03, 6.111676E+03, 6.192419E+03, 6.273682E+03, 6.355466E+03, 6.437769E+03,
6.520592E+03, 6.603933E+03, 6.687791E+03, 6.772167E+03, 6.857060E+03, 6.942467E+03,
7.028390E+03, 7.114827E+03, 7.201776E+03, 7.289238E+03, 7.377212E+03, 7.465695E+03,
7.554688E+03, 7.644190E+03, 7.734198E+03, 7.824713E+03, 7.915734E+03, 8.007258E+03,
8.099285E+03, 8.191814E+03, 8.284844E+03, 8.378373E+03, 8.472401E+03, 8.566925E+03,
8.661945E+03, 8.757460E+03, 8.853467E+03, 8.949967E+03, 9.046956E+03, 9.144435E+03,
9.242402E+03, 9.340855E+03, 9.439793E+03, 9.539214E+03, 9.639117E+03, 9.739501E+03,
9.840364E+03, 9.941705E+03, 1.004352E+04, 1.014581E+04, 1.024858E+04, 1.035181E+04,
1.045552E+04, 1.055969E+04, 1.066433E+04, 1.076944E+04, 1.087501E+04, 1.098104E+04,
1.108753E+04, 1.119448E+04, 1.130189E+04, 1.140975E+04, 1.151807E+04, 1.162684E+04,
1.173606E+04, 1.184573E+04, 1.195584E+04, 1.206641E+04, 1.217741E+04, 1.228886E+04,
1.240074E+04, 1.251307E+04, 1.262583E+04, 1.273903E+04, 1.285266E+04, 1.296672E+04,
1.308121E+04, 1.319613E+04, 1.331148E+04, 1.342725E+04, 1.354344E+04, 1.366005E+04,
1.377709E+04, 1.389454E+04, 1.401240E+04, 1.413069E+04, 1.424938E+04, 1.436848E+04,
1.448799E+04, 1.460791E+04, 1.472823E+04, 1.484896E+04, 1.497009E+04, 1.509161E+04,
1.521354E+04, 1.533586E+04, 1.545857E+04, 1.558168E+04, 1.570518E+04, 1.582906E+04,
1.595333E+04, 1.607799E+04, 1.620303E+04, 1.632846E+04, 1.645426E+04, 1.658044E+04,
1.670699E+04, 1.683392E+04, 1.696123E+04, 1.708890E+04, 1.721694E+04, 1.734535E+04,
1.747412E+04, 1.760326E+04, 1.773276E+04, 1.786262E+04, 1.799284E+04, 1.812341E+04,
1.825434E+04, 1.838562E+04, 1.851725E+04, 1.864923E+04, 1.878155E+04, 1.891423E+04,
1.904724E+04, 1.918060E+04, 1.931430E+04, 1.944833E+04, 1.958271E+04, 1.971742E+04,
1.985246E+04, 1.998783E+04, 2.012353E+04, 2.025956E+04, 2.039591E+04, 2.053259E+04,
2.066960E+04, 2.080692E+04, 2.094456E+04, 2.108252E+04, 2.122079E+04, 2.135938E+04,
2.149828E+04, 2.163749E+04, 2.177701E+04, 2.191683E+04, 2.205696E+04, 2.219740E+04,
2.233813E+04, 2.247917E+04, 2.262050E+04, 2.276213E+04, 2.290406E+04, 2.304628E+04,
2.318879E+04, 2.333159E+04, 2.347468E+04, 2.361805E+04, 2.376171E+04, 2.390565E+04,
2.404987E+04, 2.419438E+04, 2.433916E+04, 2.448422E+04, 2.462955E+04, 2.477516E+04,
2.492103E+04, 2.506718E+04, 2.521360E+04, 2.536028E+04, 2.550723E+04, 2.565444E+04,
2.580191E+04, 2.594964E+04, 2.609763E+04, 2.624588E+04, 2.639439E+04, 2.654314E+04,
2.669215E+04, 2.684141E+04, 2.699092E+04, 2.714068E+04, 2.729068E+04, 2.744093E+04,
2.759142E+04, 2.774215E+04, 2.789312E+04, 2.804433E+04, 2.819578E+04, 2.834746E+04,
2.849937E+04, 2.865152E+04, 2.880390E+04, 2.895650E+04, 2.910934E+04, 2.926240E+04,
2.941568E+04, 2.956919E+04, 2.972292E+04, 2.987687E+04, 3.003104E+04, 3.018542E+04,
3.034002E+04, 3.049484E+04, 3.064987E+04, 3.080511E+04, 3.096055E+04, 3.111621E+04,
3.127208E+04, 3.142815E+04, 3.158442E+04, 3.174090E+04, 3.189758E+04, 3.205446E+04,
3.221153E+04, 3.236881E+04, 3.252628E+04, 3.268394E+04, 3.284180E+04, 3.299985E+04,
3.315809E+04, 3.331652E+04, 3.347514E+04, 3.363394E+04, 3.379293E+04, 3.395210E+04,
3.411145E+04, 3.427098E+04, 3.443070E+04, 3.459059E+04, 3.475066E+04, 3.491090E+04,
3.507132E+04, 3.523191E+04, 3.539268E+04, 3.555361E+04, 3.571471E+04, 3.587598E+04,
3.603742E+04, 3.619902E+04, 3.636079E+04, 3.652272E+04, 3.668481E+04, 3.684706E+04,
3.700947E+04, 3.717204E+04, 3.733477E+04, 3.749765E+04, 3.766069E+04, 3.782387E+04,
3.798721E+04, 3.815070E+04, 3.831434E+04, 3.847813E+04, 3.864207E+04, 3.880615E+04,
3.897037E+04, 3.913474E+04, 3.929926E+04, 3.946391E+04, 3.962870E+04, 3.979363E+04,
3.995870E+04, 4.012391E+04, 4.028925E+04, 4.045473E+04, 4.062034E+04, 4.078608E+04,
4.095195E+04, 4.111796E+04, 4.128409E+04, 4.145035E+04, 4.161673E+04, 4.178324E+04,
4.194988E+04, 4.211664E+04, 4.228352E+04, 4.245053E+04, 4.261765E+04, 4.278489E+04,
4.295225E+04, 4.311973E+04, 4.328733E+04, 4.345503E+04, 4.362286E+04, 4.379079E+04,
4.395884E+04, 4.412700E+04, 4.429527E+04, 4.446365E+04, 4.463213E+04, 4.480073E+04,
4.496943E+04, 4.513823E+04, 4.530714E+04, 4.547615E+04, 4.564526E+04, 4.581448E+04,
4.598379E+04, 4.615320E+04, 4.632272E+04, 4.649233E+04, 4.666203E+04, 4.683183E+04,
4.700173E+04, 4.717172E+04, 4.734180E+04, 4.751197E+04, 4.768224E+04, 4.785259E+04,
4.802304E+04, 4.819357E+04, 4.836419E+04, 4.853489E+04, 4.870568E+04, 4.887656E+04,
4.904752E+04, 4.921856E+04, 4.938969E+04, 4.956089E+04, 4.973218E+04, 4.990354E+04,
5.007499E+04, 5.024651E+04, 5.041811E+04, 5.058978E+04, 5.076153E+04, 5.093336E+04,
5.110526E+04, 5.127723E+04, 5.144927E+04, 5.162139E+04, 5.179357E+04, 5.196583E+04,
5.213815E+04, 5.231054E+04, 5.248300E+04, 5.265553E+04, 5.282812E+04, 5.300078E+04,
5.317350E+04, 5.334628E+04, 5.351913E+04, 5.369204E+04, 5.386501E+04, 5.403804E+04,
5.421113E+04,
])
# ---------------------- M = 22, I = 3 ---------------------------
M = 22
I = 3
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[3]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.042930E+00, 1.565169E+01, 3.061038E+01, 4.557650E+01, 6.054536E+01, 7.551604E+01,
9.048823E+01, 1.054618E+02, 1.204367E+02, 1.354129E+02, 1.503903E+02, 1.653691E+02,
1.803492E+02, 1.953309E+02, 2.103145E+02, 2.253008E+02, 2.402907E+02, 2.552858E+02,
2.702880E+02, 2.852999E+02, 3.003246E+02, 3.153656E+02, 3.304270E+02, 3.455134E+02,
3.606296E+02, 3.757809E+02, 3.909726E+02, 4.062104E+02, 4.214999E+02, 4.368470E+02,
4.522573E+02, 4.677366E+02, 4.832903E+02, 4.989241E+02, 5.146432E+02, 5.304529E+02,
5.463581E+02, 5.623636E+02, 5.784740E+02, 5.946939E+02, 6.110273E+02, 6.274783E+02,
6.440507E+02, 6.607482E+02, 6.775743E+02, 6.945321E+02, 7.116249E+02, 7.288554E+02,
7.462266E+02, 7.637410E+02, 7.814012E+02, 7.992095E+02, 8.171680E+02, 8.352790E+02,
8.535443E+02, 8.719660E+02, 8.905456E+02, 9.092849E+02, 9.281856E+02, 9.472490E+02,
9.664766E+02, 9.858697E+02, 1.005430E+03, 1.025157E+03, 1.045054E+03, 1.065122E+03,
1.085360E+03, 1.105770E+03, 1.126354E+03, 1.147111E+03, 1.168043E+03, 1.189150E+03,
1.210433E+03, 1.231893E+03, 1.253530E+03, 1.275346E+03, 1.297340E+03, 1.319512E+03,
1.341865E+03, 1.364397E+03, 1.387110E+03, 1.410003E+03, 1.433077E+03, 1.456333E+03,
1.479771E+03, 1.503390E+03, 1.527192E+03, 1.551176E+03, 1.575343E+03, 1.599692E+03,
1.624225E+03, 1.648941E+03, 1.673840E+03, 1.698922E+03, 1.724188E+03, 1.749637E+03,
1.775269E+03, 1.801085E+03, 1.827085E+03, 1.853267E+03, 1.879634E+03, 1.906183E+03,
1.932916E+03, 1.959832E+03, 1.986931E+03, 2.014212E+03, 2.041677E+03, 2.069324E+03,
2.097154E+03, 2.125166E+03, 2.153359E+03, 2.181735E+03, 2.210292E+03, 2.239031E+03,
2.267951E+03, 2.297051E+03, 2.326332E+03, 2.355794E+03, 2.385435E+03, 2.415256E+03,
2.445256E+03, 2.475436E+03, 2.505794E+03, 2.536330E+03, 2.567044E+03, 2.597935E+03,
2.629004E+03, 2.660250E+03, 2.691671E+03, 2.723269E+03, 2.755043E+03, 2.786991E+03,
2.819114E+03, 2.851411E+03, 2.883882E+03, 2.916526E+03, 2.949343E+03, 2.982332E+03,
3.015493E+03, 3.048826E+03, 3.082329E+03, 3.116002E+03, 3.149846E+03, 3.183859E+03,
3.218040E+03, 3.252390E+03, 3.286907E+03, 3.321592E+03, 3.356443E+03, 3.391461E+03,
3.426643E+03, 3.461991E+03, 3.497504E+03, 3.533180E+03, 3.569019E+03, 3.605021E+03,
3.641186E+03, 3.677511E+03, 3.713998E+03, 3.750645E+03, 3.787452E+03, 3.824418E+03,
3.861542E+03, 3.898825E+03, 3.936265E+03, 3.973861E+03, 4.011614E+03, 4.049522E+03,
4.087585E+03, 4.125803E+03, 4.164174E+03, 4.202698E+03, 4.241375E+03, 4.280203E+03,
4.319182E+03, 4.358312E+03, 4.397592E+03, 4.437021E+03, 4.476599E+03, 4.516325E+03,
4.556198E+03, 4.596218E+03, 4.636384E+03, 4.676695E+03, 4.717151E+03, 4.757752E+03,
4.798495E+03, 4.839382E+03, 4.880411E+03, 4.921581E+03, 4.962892E+03, 5.004344E+03,
5.045935E+03, 5.087665E+03, 5.129534E+03, 5.171540E+03, 5.213683E+03, 5.255963E+03,
5.298378E+03, 5.340928E+03, 5.383613E+03, 5.426432E+03, 5.469384E+03, 5.512468E+03,
5.555684E+03, 5.599031E+03, 5.642509E+03, 5.686116E+03, 5.729853E+03, 5.773719E+03,
5.817712E+03, 5.861833E+03, 5.906080E+03, 5.950453E+03, 5.994952E+03, 6.039576E+03,
6.084323E+03, 6.129194E+03, 6.174188E+03, 6.219304E+03, 6.264541E+03, 6.309899E+03,
6.355378E+03, 6.400976E+03, 6.446693E+03, 6.492528E+03, 6.538481E+03, 6.584551E+03,
6.630738E+03, 6.677040E+03, 6.723457E+03, 6.769989E+03, 6.816635E+03, 6.863394E+03,
6.910266E+03, 6.957250E+03, 7.004345E+03, 7.051551E+03, 7.098867E+03, 7.146292E+03,
7.193827E+03, 7.241469E+03, 7.289220E+03, 7.337077E+03, 7.385041E+03, 7.433111E+03,
7.481286E+03, 7.529565E+03, 7.577949E+03, 7.626436E+03, 7.675025E+03, 7.723717E+03,
7.772511E+03, 7.821405E+03, 7.870400E+03, 7.919495E+03, 7.968689E+03, 8.017981E+03,
8.067371E+03, 8.116859E+03, 8.166444E+03, 8.216125E+03, 8.265901E+03, 8.315773E+03,
8.365739E+03, 8.415798E+03, 8.465952E+03, 8.516198E+03, 8.566536E+03, 8.616965E+03,
8.667486E+03, 8.718097E+03, 8.768798E+03, 8.819588E+03, 8.870467E+03, 8.921434E+03,
8.972489E+03, 9.023631E+03, 9.074859E+03, 9.126174E+03, 9.177573E+03, 9.229058E+03,
9.280626E+03, 9.332279E+03, 9.384014E+03, 9.435833E+03, 9.487733E+03, 9.539715E+03,
9.591778E+03, 9.643921E+03, 9.696144E+03, 9.748447E+03, 9.800828E+03, 9.853288E+03,
9.905826E+03, 9.958440E+03, 1.001113E+04, 1.006390E+04, 1.011674E+04, 1.016966E+04,
1.022266E+04, 1.027572E+04, 1.032886E+04, 1.038208E+04, 1.043536E+04, 1.048872E+04,
1.054215E+04, 1.059566E+04, 1.064923E+04, 1.070287E+04, 1.075658E+04, 1.081036E+04,
1.086421E+04, 1.091813E+04, 1.097212E+04, 1.102617E+04, 1.108029E+04, 1.113447E+04,
1.118872E+04, 1.124304E+04, 1.129742E+04, 1.135187E+04, 1.140637E+04, 1.146095E+04,
1.151558E+04, 1.157028E+04, 1.162504E+04, 1.167986E+04, 1.173474E+04, 1.178968E+04,
1.184468E+04, 1.189975E+04, 1.195487E+04, 1.201005E+04, 1.206528E+04, 1.212058E+04,
1.217593E+04, 1.223134E+04, 1.228681E+04, 1.234233E+04, 1.239791E+04, 1.245354E+04,
1.250923E+04, 1.256497E+04, 1.262077E+04, 1.267661E+04, 1.273252E+04, 1.278847E+04,
1.284448E+04, 1.290054E+04, 1.295665E+04, 1.301281E+04, 1.306902E+04, 1.312528E+04,
1.318159E+04, 1.323795E+04, 1.329436E+04, 1.335081E+04, 1.340732E+04, 1.346387E+04,
1.352047E+04, 1.357712E+04, 1.363381E+04, 1.369055E+04, 1.374734E+04, 1.380417E+04,
1.386104E+04, 1.391796E+04, 1.397492E+04, 1.403193E+04, 1.408898E+04, 1.414607E+04,
1.420321E+04, 1.426039E+04, 1.431761E+04, 1.437487E+04, 1.443217E+04, 1.448951E+04,
1.454690E+04, 1.460432E+04, 1.466178E+04, 1.471928E+04, 1.477682E+04, 1.483440E+04,
1.489202E+04, 1.494968E+04, 1.500737E+04, 1.506510E+04, 1.512287E+04, 1.518067E+04,
1.523851E+04, 1.529638E+04, 1.535429E+04, 1.541224E+04, 1.547022E+04, 1.552823E+04,
1.558628E+04, 1.564436E+04, 1.570248E+04, 1.576063E+04, 1.581881E+04, 1.587702E+04,
1.593527E+04, 1.599354E+04, 1.605185E+04, 1.611019E+04, 1.616856E+04, 1.622697E+04,
1.628540E+04, 1.634386E+04, 1.640235E+04, 1.646087E+04, 1.651942E+04, 1.657800E+04,
1.663660E+04, 1.669524E+04, 1.675390E+04, 1.681259E+04, 1.687130E+04, 1.693005E+04,
1.698882E+04, 1.704761E+04, 1.710643E+04, 1.716528E+04, 1.722415E+04, 1.728305E+04,
1.734197E+04, 1.740092E+04, 1.745989E+04, 1.751889E+04, 1.757791E+04, 1.763695E+04,
1.769602E+04, 1.775510E+04, 1.781422E+04, 1.787335E+04, 1.793250E+04, 1.799168E+04,
1.805088E+04, 1.811010E+04, 1.816934E+04, 1.822860E+04, 1.828789E+04, 1.834719E+04,
1.840651E+04, 1.846585E+04, 1.852522E+04, 1.858460E+04, 1.864400E+04, 1.870342E+04,
1.876285E+04,
])
# ---------------------- M = 23, I = 1 ---------------------------
M = 23
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
6.255910E+00, 5.846743E+01, 1.148737E+02, 1.712988E+02, 2.277330E+02, 2.841901E+02,
3.407448E+02, 3.975806E+02, 4.549981E+02, 5.133834E+02, 5.731623E+02, 6.347625E+02,
6.985902E+02, 7.650186E+02, 8.343861E+02, 9.069982E+02, 9.831325E+02, 1.063044E+03,
1.146969E+03, 1.235132E+03, 1.327747E+03, 1.425022E+03, 1.527162E+03, 1.634371E+03,
1.746850E+03, 1.864805E+03, 1.988442E+03, 2.117972E+03, 2.253608E+03, 2.395569E+03,
2.544078E+03, 2.699363E+03, 2.861660E+03, 3.031206E+03, 3.208249E+03, 3.393039E+03,
3.585834E+03, 3.786899E+03, 3.996503E+03, 4.214924E+03, 4.442444E+03, 4.679352E+03,
4.925946E+03, 5.182528E+03, 5.449407E+03, 5.726899E+03, 6.015328E+03, 6.315021E+03,
6.626316E+03, 6.949556E+03, 7.285090E+03, 7.644438E+03, 8.017771E+03, 8.405197E+03,
8.807106E+03, 9.223898E+03, 9.655973E+03, 1.010375E+04, 1.056765E+04, 1.104809E+04,
1.154553E+04, 1.206039E+04, 1.259313E+04, 1.314422E+04, 1.371412E+04, 1.430330E+04,
1.491225E+04, 1.554147E+04, 1.619144E+04, 1.686268E+04, 1.755572E+04, 1.827106E+04,
1.900924E+04, 1.977081E+04, 2.055631E+04, 2.136630E+04, 2.220136E+04, 2.306204E+04,
2.394895E+04, 2.486265E+04, 2.580377E+04, 2.677291E+04, 2.777069E+04, 2.879773E+04,
2.985468E+04, 3.094216E+04, 3.206084E+04, 3.321139E+04, 3.439446E+04, 3.561075E+04,
3.686094E+04, 3.814573E+04, 3.946582E+04, 4.082193E+04, 4.221480E+04, 4.364516E+04,
4.511375E+04, 4.662130E+04, 4.816861E+04, 4.975646E+04, 5.138558E+04, 5.305681E+04,
5.477092E+04, 5.652873E+04, 5.833107E+04, 6.017876E+04, 6.207265E+04, 6.401357E+04,
6.600238E+04, 6.803996E+04, 7.012718E+04, 7.226494E+04, 7.445411E+04, 7.669563E+04,
7.899039E+04, 8.133932E+04, 8.374336E+04, 8.620347E+04, 8.872059E+04, 9.129568E+04,
9.392974E+04, 9.662374E+04, 9.937866E+04, 1.021955E+05, 1.050753E+05, 1.080191E+05,
1.110280E+05, 1.141028E+05, 1.172448E+05, 1.204550E+05, 1.237344E+05, 1.270842E+05,
1.305054E+05, 1.339991E+05, 1.375665E+05, 1.412087E+05, 1.449268E+05, 1.487220E+05,
1.525953E+05, 1.565481E+05, 1.605814E+05, 1.646964E+05, 1.688944E+05, 1.731765E+05,
1.775439E+05, 1.819979E+05, 1.865398E+05, 1.911707E+05, 1.958919E+05, 2.007047E+05,
2.056102E+05, 2.106100E+05, 2.157051E+05, 2.208970E+05, 2.261869E+05, 2.315762E+05,
2.370662E+05, 2.426582E+05, 2.483535E+05, 2.541537E+05, 2.600599E+05, 2.660736E+05,
2.721962E+05, 2.784291E+05, 2.847737E+05, 2.912314E+05, 2.978036E+05, 3.044918E+05,
3.112975E+05, 3.182219E+05, 3.252667E+05, 3.324334E+05, 3.397233E+05, 3.471381E+05,
3.546790E+05, 3.623478E+05,
])
# ---------------------- M = 23, I = 2 ---------------------------
M = 23
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.256912E+01, 1.198179E+02, 2.355169E+02, 3.512532E+02, 4.670077E+02, 5.828098E+02,
6.988156E+02, 8.154062E+02, 9.332049E+02, 1.053010E+03, 1.175699E+03, 1.302157E+03,
1.433218E+03, 1.569655E+03, 1.712166E+03, 1.861382E+03, 2.017879E+03, 2.182186E+03,
2.354797E+03, 2.536179E+03, 2.726782E+03, 2.927041E+03, 3.137388E+03, 3.358249E+03,
3.590052E+03, 3.833228E+03, 4.088213E+03, 4.355451E+03, 4.635393E+03, 4.928501E+03,
5.235245E+03, 5.556109E+03, 5.891584E+03, 6.242178E+03, 6.608407E+03, 6.990802E+03,
7.389906E+03, 7.806274E+03, 8.240476E+03, 8.693093E+03, 9.164721E+03, 9.655968E+03,
1.016746E+04, 1.069982E+04, 1.125372E+04, 1.182980E+04, 1.242875E+04, 1.305125E+04,
1.369802E+04, 1.436977E+04, 1.506723E+04, 1.581858E+04, 1.659528E+04, 1.740150E+04,
1.823806E+04, 1.910581E+04, 2.000560E+04, 2.093831E+04, 2.190483E+04, 2.290607E+04,
2.394295E+04, 2.501642E+04, 2.612743E+04, 2.727695E+04, 2.846597E+04, 2.969552E+04,
3.096661E+04, 3.228030E+04, 3.363762E+04, 3.503967E+04, 3.648755E+04, 3.798238E+04,
3.952527E+04, 4.111738E+04, 4.275989E+04, 4.445397E+04, 4.620085E+04, 4.800173E+04,
4.985785E+04, 5.177049E+04, 5.374092E+04, 5.577045E+04, 5.786038E+04, 6.001205E+04,
6.222683E+04, 6.450609E+04, 6.685123E+04, 6.926364E+04, 7.174476E+04, 7.429607E+04,
7.691903E+04, 7.961513E+04, 8.238587E+04, 8.523279E+04, 8.815748E+04, 9.116146E+04,
9.424635E+04, 9.741373E+04, 1.006653E+05, 1.040027E+05, 1.074275E+05, 1.109415E+05,
1.145465E+05, 1.182440E+05, 1.220360E+05, 1.259241E+05, 1.299102E+05, 1.339961E+05,
1.381836E+05, 1.424746E+05, 1.468710E+05, 1.513747E+05, 1.559876E+05, 1.607117E+05,
1.655489E+05, 1.705012E+05, 1.755707E+05, 1.807593E+05, 1.860692E+05, 1.915024E+05,
1.970610E+05, 2.027471E+05, 2.085628E+05, 2.145104E+05, 2.205921E+05, 2.268099E+05,
2.331663E+05, 2.396632E+05, 2.463032E+05, 2.530885E+05, 2.600213E+05, 2.671041E+05,
2.743392E+05, 2.817289E+05, 2.892757E+05, 2.969820E+05, 3.048502E+05, 3.128829E+05,
3.210824E+05, 3.294513E+05, 3.379922E+05, 3.467075E+05, 3.556000E+05, 3.646721E+05,
3.739265E+05, 3.833658E+05, 3.929927E+05, 4.028098E+05, 4.128199E+05, 4.230257E+05,
4.334300E+05, 4.440355E+05, 4.548450E+05, 4.658613E+05, 4.770871E+05, 4.885256E+05,
5.001793E+05, 5.120512E+05, 5.241444E+05, 5.364618E+05, 5.490060E+05, 5.617803E+05,
5.747876E+05, 5.880310E+05, 6.015134E+05, 6.152380E+05, 6.292077E+05, 6.434256E+05,
6.578952E+05, 6.726192E+05, 6.876007E+05, 7.028432E+05, 7.183499E+05, 7.341236E+05,
7.501680E+05, 7.664860E+05,
])
# ---------------------- M = 23, I = 3 ---------------------------
M = 23
I = 3
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
4.705510E+00, 4.011176E+01, 7.886627E+01, 1.176487E+02, 1.564527E+02, 1.952881E+02,
2.342069E+02, 2.733368E+02, 3.128870E+02, 3.531270E+02, 3.943565E+02, 4.368808E+02,
4.809954E+02, 5.269788E+02, 5.750913E+02, 6.255753E+02, 6.786581E+02, 7.345535E+02,
7.934654E+02, 8.555889E+02, 9.211128E+02, 9.902210E+02, 1.063094E+03, 1.139909E+03,
1.220842E+03, 1.306069E+03, 1.395765E+03, 1.490107E+03, 1.589273E+03, 1.693442E+03,
1.802795E+03, 1.917518E+03, 2.037796E+03, 2.163821E+03, 2.295788E+03, 2.433892E+03,
2.578337E+03, 2.729329E+03, 2.887077E+03, 3.051796E+03, 3.223707E+03, 3.403033E+03,
3.590003E+03, 3.784852E+03, 3.987816E+03, 4.199140E+03, 4.419076E+03, 4.647874E+03,
4.885797E+03, 5.133106E+03, 5.390076E+03, 5.656980E+03, 5.934099E+03, 6.221720E+03,
6.520136E+03, 6.829645E+03, 7.150549E+03, 7.483158E+03, 7.827786E+03, 8.184755E+03,
8.554390E+03, 8.937025E+03, 9.332999E+03, 9.742652E+03, 1.016634E+04, 1.060441E+04,
1.105723E+04, 1.152517E+04, 1.200860E+04, 1.250790E+04, 1.302346E+04, 1.355567E+04,
1.410492E+04, 1.467163E+04, 1.525621E+04, 1.585907E+04, 1.648063E+04, 1.712133E+04,
1.778161E+04, 1.846190E+04, 1.916266E+04, 1.988433E+04, 2.062740E+04, 2.139232E+04,
2.217957E+04, 2.298962E+04, 2.382298E+04, 2.468014E+04, 2.556160E+04, 2.646787E+04,
2.739946E+04, 2.835689E+04, 2.934071E+04, 3.035144E+04, 3.138963E+04, 3.245582E+04,
3.355059E+04, 3.467448E+04, 3.582807E+04, 3.701195E+04, 3.822668E+04, 3.947288E+04,
4.075113E+04, 4.206204E+04, 4.340624E+04, 4.478434E+04, 4.619696E+04, 4.764476E+04,
4.912835E+04, 5.064840E+04, 5.220557E+04, 5.380051E+04, 5.543391E+04, 5.710644E+04,
5.881878E+04, 6.057165E+04, 6.236571E+04, 6.420171E+04, 6.608034E+04, 6.800233E+04,
6.996841E+04, 7.197933E+04, 7.403584E+04, 7.613867E+04, 7.828858E+04, 8.048637E+04,
8.273280E+04, 8.502866E+04, 8.737470E+04, 8.977178E+04, 9.222067E+04, 9.472220E+04,
9.727719E+04, 9.988645E+04, 1.025508E+05, 1.052712E+05, 1.080484E+05, 1.108832E+05,
1.137766E+05, 1.167294E+05, 1.197425E+05, 1.228168E+05, 1.259531E+05, 1.291525E+05,
1.324157E+05, 1.357438E+05, 1.391376E+05, 1.425981E+05, 1.461262E+05, 1.497229E+05,
1.533892E+05, 1.571259E+05, 1.609341E+05, 1.648147E+05, 1.687687E+05, 1.727973E+05,
1.769011E+05, 1.810815E+05, 1.853392E+05, 1.896755E+05, 1.940913E+05, 1.985876E+05,
2.031654E+05, 2.078260E+05, 2.125702E+05, 2.173992E+05, 2.223140E+05, 2.273157E+05,
2.324055E+05, 2.375843E+05, 2.428533E+05, 2.482137E+05, 2.536665E+05, 2.592128E+05,
2.648538E+05, 2.705906E+05,
])
# ---------------------- M = 24, I = 1 ---------------------------
M = 24
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
2.292033E+01, 1.005064E+03, 2.758565E+03, 5.052901E+03, 7.773275E+03, 1.085963E+04,
1.427439E+04, 1.799370E+04, 2.200520E+04, 2.630720E+04, 3.090790E+04, 3.582448E+04,
4.108212E+04, 4.671306E+04, 5.275584E+04, 5.925467E+04, 6.625898E+04, 7.382313E+04,
8.200629E+04, 9.087247E+04, 1.004906E+05, 1.109347E+05, 1.222845E+05, 1.346250E+05,
1.480481E+05, 1.626518E+05, 1.785416E+05, 1.958306E+05, 2.146405E+05, 2.351016E+05,
2.573542E+05, 2.815485E+05, 3.078462E+05, 3.364206E+05, 3.674579E+05, 4.011576E+05,
4.377339E+05, 4.774164E+05, 5.204511E+05, 5.671017E+05, 6.176507E+05, 6.724001E+05,
7.316734E+05, 7.958165E+05, 8.651993E+05, 9.402168E+05, 1.021291E+06, 1.108873E+06,
1.203444E+06, 1.305515E+06, 1.415636E+06, 1.534387E+06, 1.662391E+06, 1.800308E+06,
1.948842E+06, 2.108742E+06, 2.280804E+06, 2.465875E+06, 2.664853E+06, 2.878694E+06,
3.108412E+06, 3.355083E+06, 3.619849E+06, 3.903920E+06, 4.208579E+06, 4.535186E+06,
4.885181E+06, 5.260089E+06, 5.661522E+06, 6.091189E+06, 6.550894E+06, 7.042545E+06,
7.568161E+06, 8.129872E+06, 8.729927E+06, 9.370703E+06, 1.005471E+07, 1.078458E+07,
1.156312E+07, 1.239325E+07, 1.327809E+07, 1.422089E+07, 1.522509E+07, 1.629431E+07,
1.743237E+07, 1.864327E+07, 1.993123E+07, 2.130068E+07, 2.275629E+07, 2.430295E+07,
2.594581E+07, 2.769028E+07, 2.954203E+07, 3.150702E+07, 3.359151E+07, 3.580204E+07,
3.814551E+07, 4.062912E+07, 4.326044E+07, 4.604738E+07, 4.899825E+07, 5.212174E+07,
5.542695E+07, 5.892342E+07, 6.262112E+07, 6.653048E+07, 7.066241E+07, 7.502834E+07,
7.964020E+07, 8.451045E+07, 8.965214E+07, 9.507889E+07, 1.008049E+08, 1.068451E+08,
1.132149E+08, 1.199306E+08, 1.270089E+08, 1.344676E+08, 1.423251E+08, 1.506005E+08,
1.593137E+08, 1.684857E+08, 1.781381E+08, 1.882936E+08, 1.989757E+08, 2.102091E+08,
2.220191E+08, 2.344325E+08, 2.474770E+08, 2.611813E+08, 2.755755E+08, 2.906907E+08,
3.065595E+08, 3.232155E+08, 3.406938E+08, 3.590310E+08, 3.782647E+08, 3.984346E+08,
4.195814E+08, 4.417476E+08, 4.649774E+08, 4.893165E+08, 5.148126E+08, 5.415149E+08,
5.694747E+08, 5.987452E+08, 6.293814E+08, 6.614407E+08, 6.949822E+08, 7.300674E+08,
7.667602E+08, 8.051266E+08, 8.452350E+08, 8.871565E+08, 9.309645E+08, 9.767353E+08,
1.024548E+09, 1.074483E+09, 1.126626E+09, 1.181065E+09, 1.237890E+09, 1.297194E+09,
1.359076E+09, 1.423634E+09, 1.490973E+09, 1.561201E+09, 1.634429E+09, 1.710770E+09,
1.790345E+09, 1.873276E+09, 1.959690E+09, 2.049717E+09, 2.143494E+09, 2.241160E+09,
2.342859E+09, 2.448743E+09,
])
# ---------------------- M = 24, I = 2 ---------------------------
M = 24
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
2.320610E+01, 1.020802E+03, 2.802064E+03, 5.132731E+03, 7.896193E+03, 1.103144E+04,
1.450032E+04, 1.827857E+04, 2.235364E+04, 2.672383E+04, 3.139746E+04, 3.639199E+04,
4.173298E+04, 4.745320E+04, 5.359179E+04, 6.019368E+04, 6.730905E+04, 7.499315E+04,
8.330609E+04, 9.231288E+04, 1.020835E+05, 1.126934E+05, 1.242231E+05, 1.367594E+05,
1.503953E+05, 1.652307E+05, 1.813726E+05, 1.989359E+05, 2.180442E+05, 2.388299E+05,
2.614355E+05, 2.860137E+05, 3.127287E+05, 3.417565E+05, 3.732862E+05, 4.075207E+05,
4.446774E+05, 4.849896E+05, 5.287073E+05, 5.760983E+05, 6.274494E+05, 6.830678E+05,
7.432819E+05, 8.084431E+05, 8.789272E+05, 9.551356E+05, 1.037497E+06, 1.126469E+06,
1.222541E+06, 1.326233E+06, 1.438102E+06, 1.558738E+06, 1.688774E+06, 1.828881E+06,
1.979774E+06, 2.142213E+06, 2.317007E+06, 2.505017E+06, 2.707155E+06, 2.924392E+06,
3.157758E+06, 3.408347E+06, 3.677317E+06, 3.965900E+06, 4.275399E+06, 4.607193E+06,
4.962748E+06, 5.343611E+06, 5.751421E+06, 6.187913E+06, 6.654922E+06, 7.154384E+06,
7.688351E+06, 8.258986E+06, 8.868575E+06, 9.519533E+06, 1.021440E+07, 1.095588E+07,
1.174678E+07, 1.259011E+07, 1.348901E+07, 1.444679E+07, 1.546695E+07, 1.655317E+07,
1.770932E+07, 1.893946E+07, 2.024790E+07, 2.163912E+07, 2.311786E+07, 2.468911E+07,
2.635809E+07, 2.813029E+07, 3.001148E+07, 3.200771E+07, 3.412534E+07, 3.637102E+07,
3.875175E+07, 4.127485E+07, 4.394801E+07, 4.677927E+07, 4.977706E+07, 5.295023E+07,
5.630800E+07, 5.986008E+07, 6.361659E+07, 6.758813E+07, 7.178578E+07, 7.622116E+07,
8.090637E+07, 8.585410E+07, 9.107759E+07, 9.659067E+07, 1.024078E+08, 1.085441E+08,
1.150152E+08, 1.218377E+08, 1.290287E+08, 1.366061E+08, 1.445886E+08, 1.529956E+08,
1.618476E+08, 1.711655E+08, 1.809715E+08, 1.912887E+08, 2.021408E+08, 2.135529E+08,
2.255509E+08, 2.381619E+08, 2.514140E+08, 2.653365E+08, 2.799598E+08, 2.953157E+08,
3.114371E+08, 3.283583E+08, 3.461149E+08, 3.647439E+08, 3.842840E+08, 4.047750E+08,
4.262585E+08, 4.487777E+08, 4.723774E+08, 4.971041E+08, 5.230062E+08, 5.501338E+08,
5.785389E+08, 6.082756E+08, 6.393998E+08, 6.719697E+08, 7.060455E+08, 7.416896E+08,
7.789669E+08, 8.179445E+08, 8.586919E+08, 9.012813E+08, 9.457873E+08, 9.922872E+08,
1.040861E+09, 1.091593E+09, 1.144567E+09, 1.199873E+09, 1.257603E+09, 1.317853E+09,
1.380720E+09, 1.446308E+09, 1.514720E+09, 1.586067E+09, 1.660462E+09, 1.738021E+09,
1.818864E+09, 1.903117E+09, 1.990908E+09, 2.082370E+09, 2.177642E+09, 2.276865E+09,
2.380187E+09, 2.487758E+09,
])
# ---------------------- M = 25, I = 1 ---------------------------
M = 25
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[1]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.770090E+00, 9.937734E+01, 3.215728E+02, 6.242169E+02, 9.940464E+02, 1.428783E+03,
1.930438E+03, 2.502407E+03, 3.148468E+03, 3.872571E+03, 4.678893E+03, 5.571933E+03,
6.556583E+03, 7.638169E+03, 8.822458E+03, 1.011566E+04, 1.152440E+04, 1.305573E+04,
1.471706E+04, 1.651621E+04, 1.846133E+04, 2.056093E+04, 2.282389E+04, 2.525940E+04,
2.787700E+04, 3.068660E+04, 3.369842E+04, 3.692304E+04, 4.037142E+04, 4.405485E+04,
4.798501E+04, 5.217394E+04, 5.663409E+04, 6.137826E+04, 6.641968E+04, 7.177196E+04,
7.744912E+04, 8.346559E+04, 8.983619E+04, 9.657618E+04, 1.037012E+05, 1.112273E+05,
1.191709E+05, 1.275489E+05, 1.363785E+05, 1.456772E+05, 1.554631E+05, 1.657543E+05,
1.765695E+05, 1.879275E+05, 1.998476E+05, 2.123492E+05, 2.254519E+05, 2.391757E+05,
2.535406E+05, 2.685667E+05, 2.842744E+05, 3.006842E+05, 3.178165E+05, 3.356919E+05,
3.543309E+05, 3.737539E+05, 3.939815E+05, 4.150340E+05, 4.369316E+05, 4.596945E+05,
4.833425E+05, 5.078955E+05, 5.333729E+05, 5.597939E+05, 5.871776E+05, 6.155426E+05,
6.449072E+05, 6.752896E+05, 7.067073E+05, 7.391776E+05, 7.727174E+05, 8.073431E+05,
8.430708E+05, 8.799161E+05, 9.178940E+05, 9.570193E+05, 9.973060E+05, 1.038768E+06,
1.081418E+06, 1.125270E+06, 1.170335E+06, 1.216625E+06, 1.264150E+06, 1.312923E+06,
1.362953E+06, 1.414249E+06, 1.466821E+06, 1.520677E+06, 1.575826E+06, 1.632274E+06,
1.690030E+06, 1.749098E+06, 1.809486E+06, 1.871200E+06, 1.934243E+06, 1.998620E+06,
2.064337E+06, 2.131395E+06, 2.199799E+06, 2.269551E+06, 2.340654E+06, 2.413108E+06,
2.486916E+06, 2.562078E+06, 2.638594E+06, 2.716466E+06, 2.795692E+06, 2.876272E+06,
2.958205E+06, 3.041488E+06, 3.126122E+06, 3.212102E+06, 3.299428E+06, 3.388095E+06,
3.478101E+06, 3.569442E+06, 3.662114E+06, 3.756113E+06, 3.851435E+06, 3.948076E+06,
4.046029E+06, 4.145290E+06, 4.245854E+06, 4.347714E+06, 4.450865E+06, 4.555301E+06,
4.661014E+06, 4.767999E+06, 4.876249E+06, 4.985756E+06, 5.096513E+06, 5.208514E+06,
5.321749E+06, 5.436213E+06, 5.551896E+06, 5.668791E+06, 5.786890E+06, 5.906183E+06,
6.026663E+06, 6.148322E+06, 6.271150E+06, 6.395138E+06, 6.520279E+06, 6.646561E+06,
6.773978E+06, 6.902519E+06, 7.032175E+06, 7.162937E+06, 7.294795E+06, 7.427739E+06,
7.561761E+06, 7.696851E+06, 7.832999E+06, 7.970195E+06, 8.108429E+06, 8.247692E+06,
8.387974E+06, 8.529265E+06, 8.671555E+06, 8.814834E+06, 8.959093E+06, 9.104320E+06,
9.250507E+06, 9.397644E+06, 9.545720E+06, 9.694725E+06, 9.844650E+06, 9.995484E+06,
1.014722E+07, 1.029984E+07, 1.045334E+07, 1.060772E+07, 1.076295E+07, 1.091903E+07,
1.107595E+07, 1.123370E+07, 1.139227E+07, 1.155166E+07, 1.171184E+07, 1.187281E+07,
1.203457E+07, 1.219709E+07, 1.236038E+07, 1.252442E+07, 1.268921E+07, 1.285472E+07,
1.302096E+07, 1.318792E+07, 1.335558E+07, 1.352393E+07, 1.369297E+07, 1.386269E+07,
1.403307E+07, 1.420412E+07, 1.437581E+07, 1.454814E+07, 1.472111E+07, 1.489470E+07,
1.506890E+07, 1.524370E+07, 1.541910E+07, 1.559509E+07, 1.577166E+07, 1.594880E+07,
1.612649E+07, 1.630475E+07, 1.648354E+07, 1.666287E+07, 1.684273E+07, 1.702311E+07,
1.720400E+07, 1.738539E+07, 1.756728E+07, 1.774966E+07, 1.793251E+07, 1.811584E+07,
1.829962E+07, 1.848387E+07, 1.866856E+07, 1.885369E+07, 1.903926E+07, 1.922525E+07,
1.941166E+07, 1.959847E+07, 1.978569E+07, 1.997331E+07, 2.016131E+07, 2.034970E+07,
2.053846E+07, 2.072759E+07, 2.091708E+07, 2.110692E+07, 2.129710E+07, 2.148763E+07,
2.167849E+07, 2.186967E+07, 2.206118E+07, 2.225300E+07, 2.244512E+07, 2.263755E+07,
2.283027E+07, 2.302327E+07, 2.321656E+07, 2.341012E+07, 2.360396E+07, 2.379805E+07,
2.399240E+07, 2.418700E+07, 2.438185E+07, 2.457694E+07, 2.477226E+07, 2.496781E+07,
2.516358E+07, 2.535956E+07, 2.555576E+07, 2.575216E+07, 2.594876E+07, 2.614556E+07,
2.634255E+07, 2.653972E+07, 2.673707E+07, 2.693459E+07, 2.713228E+07, 2.733013E+07,
2.752815E+07, 2.772631E+07, 2.792462E+07, 2.812308E+07, 2.832168E+07, 2.852041E+07,
2.871927E+07, 2.891825E+07, 2.911736E+07, 2.931658E+07, 2.951591E+07, 2.971535E+07,
2.991488E+07, 3.011452E+07, 3.031425E+07, 3.051407E+07, 3.071398E+07, 3.091396E+07,
3.111402E+07, 3.131416E+07, 3.151436E+07, 3.171463E+07, 3.191495E+07, 3.211534E+07,
3.231577E+07, 3.251626E+07, 3.271679E+07, 3.291736E+07, 3.311797E+07, 3.331861E+07,
3.351929E+07,
])
# ---------------------- M = 26, I = 1 ---------------------------
M = 26
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[0]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.304830E+00, 2.430655E+01, 4.793073E+01, 7.156039E+01, 9.519594E+01, 1.188684E+02,
1.426920E+02, 1.668995E+02, 1.918282E+02, 2.178787E+02, 2.454753E+02, 2.750411E+02,
3.069871E+02, 3.417113E+02, 3.796013E+02, 4.210401E+02, 4.664113E+02, 5.161047E+02,
5.705201E+02, 6.300721E+02, 6.951927E+02, 7.663344E+02, 8.439731E+02, 9.286097E+02,
1.020773E+03, 1.121022E+03, 1.229946E+03, 1.348169E+03, 1.476351E+03, 1.615189E+03,
1.765420E+03, 1.927822E+03, 2.103220E+03, 2.292482E+03, 2.496526E+03, 2.716320E+03,
2.952886E+03, 3.207303E+03, 3.480706E+03, 3.774293E+03, 4.089326E+03, 4.427131E+03,
4.789107E+03, 5.176724E+03, 5.591528E+03, 6.035144E+03, 6.509280E+03, 7.015728E+03,
7.556371E+03, 8.133184E+03, 8.748238E+03, 9.403705E+03, 1.010186E+04, 1.084508E+04,
1.163587E+04, 1.247683E+04, 1.337069E+04, 1.432031E+04, 1.532865E+04, 1.639884E+04,
1.753411E+04, 1.873786E+04, 2.001359E+04, 2.136500E+04, 2.279588E+04, 2.431024E+04,
2.591218E+04, 2.760600E+04, 2.939617E+04, 3.128728E+04, 3.328414E+04, 3.539170E+04,
3.761510E+04, 3.995963E+04, 4.243078E+04, 4.503422E+04, 4.777578E+04, 5.066149E+04,
5.369755E+04, 5.689035E+04, 6.024647E+04, 6.377264E+04, 6.747581E+04, 7.136309E+04,
7.544178E+04, 7.971935E+04, 8.420347E+04, 8.890196E+04, 9.382283E+04, 9.897425E+04,
1.043646E+05, 1.100024E+05, 1.158962E+05, 1.220551E+05, 1.284878E+05, 1.352037E+05,
1.422121E+05, 1.495223E+05, 1.571439E+05, 1.650868E+05, 1.733608E+05, 1.819759E+05,
1.909421E+05, 2.002699E+05, 2.099695E+05, 2.200513E+05, 2.305261E+05, 2.414044E+05,
2.526970E+05, 2.644148E+05, 2.765688E+05, 2.891699E+05, 3.022293E+05, 3.157581E+05,
3.297675E+05, 3.442689E+05, 3.592735E+05, 3.747927E+05, 3.908380E+05, 4.074208E+05,
4.245525E+05, 4.422448E+05, 4.605090E+05, 4.793568E+05, 4.987996E+05, 5.188490E+05,
5.395165E+05, 5.608137E+05, 5.827520E+05, 6.053430E+05, 6.285981E+05, 6.525288E+05,
6.771464E+05, 7.024624E+05, 7.284880E+05, 7.552345E+05, 7.827132E+05, 8.109352E+05,
8.399115E+05, 8.696534E+05, 9.001717E+05, 9.314773E+05, 9.635811E+05, 9.964938E+05,
1.030226E+06, 1.064788E+06, 1.100191E+06, 1.136445E+06, 1.173561E+06, 1.211548E+06,
1.250416E+06, 1.290176E+06, 1.330837E+06, 1.372410E+06, 1.414904E+06, 1.458328E+06,
1.502692E+06, 1.548005E+06, 1.594276E+06, 1.641515E+06, 1.689730E+06, 1.738930E+06,
1.789124E+06, 1.840321E+06, 1.892528E+06, 1.945754E+06, 2.000007E+06, 2.055296E+06,
2.111627E+06, 2.169010E+06, 2.227450E+06, 2.286957E+06, 2.347537E+06, 2.409197E+06,
2.471944E+06, 2.535786E+06, 2.600728E+06, 2.666778E+06, 2.733942E+06, 2.802227E+06,
2.871638E+06, 2.942181E+06, 3.013863E+06, 3.086689E+06, 3.160664E+06, 3.235795E+06,
3.312086E+06, 3.389542E+06, 3.468170E+06, 3.547972E+06, 3.628955E+06, 3.711122E+06,
3.794479E+06, 3.879029E+06, 3.964776E+06, 4.051725E+06, 4.139880E+06, 4.229243E+06,
4.319820E+06, 4.411612E+06, 4.504625E+06, 4.598859E+06, 4.694320E+06, 4.791009E+06,
4.888930E+06, 4.988084E+06, 5.088475E+06, 5.190105E+06, 5.292976E+06, 5.397090E+06,
5.502450E+06, 5.609057E+06, 5.716912E+06, 5.826018E+06, 5.936376E+06, 6.047987E+06,
6.160852E+06, 6.274973E+06, 6.390351E+06, 6.506986E+06, 6.624880E+06, 6.744032E+06,
6.864444E+06, 6.986115E+06, 7.109047E+06, 7.233239E+06, 7.358692E+06, 7.485405E+06,
7.613378E+06, 7.742611E+06, 7.873104E+06, 8.004857E+06, 8.137868E+06, 8.272137E+06,
8.407663E+06, 8.544446E+06, 8.682485E+06, 8.821778E+06, 8.962325E+06, 9.104124E+06,
9.247174E+06, 9.391473E+06, 9.537021E+06, 9.683815E+06, 9.831853E+06, 9.981135E+06,
1.013166E+07, 1.028342E+07, 1.043642E+07, 1.059066E+07, 1.074612E+07,
])
# ---------------------- M = 26, I = 2 ---------------------------
M = 26
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
8.814510E+00, 9.724635E+01, 1.917632E+02, 2.864734E+02, 3.811700E+02, 4.760528E+02,
5.715776E+02, 6.686900E+02, 7.687384E+02, 8.733388E+02, 9.841987E+02, 1.103046E+03,
1.231520E+03, 1.371261E+03, 1.523833E+03, 1.690802E+03, 1.873730E+03, 2.074207E+03,
2.293885E+03, 2.534441E+03, 2.797671E+03, 3.085414E+03, 3.399618E+03, 3.742372E+03,
4.115810E+03, 4.522255E+03, 4.964139E+03, 5.444011E+03, 5.964565E+03, 6.528734E+03,
7.139491E+03, 7.800087E+03, 8.513884E+03, 9.284496E+03, 1.011567E+04, 1.101143E+04,
1.197594E+04, 1.301371E+04, 1.412933E+04, 1.532784E+04, 1.661437E+04, 1.799441E+04,
1.947370E+04, 2.105830E+04, 2.275461E+04, 2.456929E+04, 2.650943E+04, 2.858230E+04,
3.079576E+04, 3.315789E+04, 3.567723E+04, 3.836273E+04, 4.122372E+04, 4.427006E+04,
4.751198E+04, 5.096026E+04, 5.462619E+04, 5.852141E+04, 6.265841E+04, 6.704991E+04,
7.170941E+04, 7.665096E+04, 8.188914E+04, 8.743929E+04, 9.331736E+04, 9.953997E+04,
1.061245E+05, 1.130890E+05, 1.204523E+05, 1.282340E+05, 1.364547E+05, 1.451355E+05,
1.542986E+05, 1.639671E+05, 1.741648E+05, 1.849166E+05, 1.962486E+05, 2.081876E+05,
2.207615E+05, 2.339994E+05, 2.479314E+05, 2.625888E+05, 2.780041E+05, 2.942110E+05,
3.112445E+05, 3.291406E+05, 3.479371E+05, 3.676729E+05, 3.883884E+05, 4.101252E+05,
4.329270E+05, 4.568381E+05, 4.819053E+05, 5.081766E+05, 5.357017E+05, 5.645321E+05,
5.947207E+05, 6.263228E+05, 6.593952E+05, 6.939969E+05, 7.301883E+05, 7.680325E+05,
8.075944E+05, 8.489406E+05, 8.921407E+05, 9.372660E+05, 9.843902E+05, 1.033589E+06,
1.084942E+06, 1.138530E+06, 1.194435E+06, 1.252745E+06, 1.313548E+06, 1.376935E+06,
1.443002E+06, 1.511846E+06, 1.583566E+06, 1.658265E+06, 1.736052E+06, 1.817034E+06,
1.901325E+06, 1.989042E+06, 2.080302E+06, 2.175231E+06, 2.273953E+06, 2.376600E+06,
2.483305E+06, 2.594207E+06, 2.709447E+06, 2.829171E+06, 2.953528E+06, 3.082674E+06,
3.216765E+06, 3.355965E+06, 3.500442E+06, 3.650366E+06, 3.805915E+06, 3.967268E+06,
4.134614E+06, 4.308144E+06, 4.488050E+06, 4.674537E+06, 4.867812E+06, 5.068086E+06,
5.275577E+06, 5.490508E+06, 5.713108E+06, 5.943613E+06, 6.182262E+06, 6.429305E+06,
6.684995E+06, 6.949588E+06, 7.223356E+06, 7.506568E+06, 7.799506E+06, 8.102456E+06,
8.415711E+06, 8.739574E+06, 9.074353E+06, 9.420361E+06, 9.777928E+06, 1.014738E+07,
1.052905E+07, 1.092330E+07, 1.133048E+07, 1.175095E+07, 1.218509E+07, 1.263327E+07,
1.309589E+07, 1.357335E+07, 1.406605E+07, 1.457441E+07, 1.509887E+07, 1.563985E+07,
1.619781E+07, 1.677320E+07,
])
# ---------------------- M = 26, I = 3 ---------------------------
M = 26
I = 3
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.044140E+00, 8.016384E+01, 1.642897E+02, 2.484331E+02, 3.326393E+02, 4.172555E+02,
5.031646E+02, 5.918139E+02, 6.849749E+02, 7.845357E+02, 8.923827E+02, 1.010349E+03,
1.140206E+03, 1.283685E+03, 1.442530E+03, 1.618488E+03, 1.813370E+03, 2.029067E+03,
2.267536E+03, 2.550029E+03, 2.842636E+03, 3.164913E+03, 3.519348E+03, 3.908620E+03,
4.335576E+03, 4.803251E+03, 5.314814E+03, 5.873741E+03, 6.483650E+03, 7.148457E+03,
7.872235E+03, 8.659382E+03, 9.514548E+03, 1.044272E+04, 1.144906E+04, 1.253918E+04,
1.371897E+04, 1.499462E+04, 1.637280E+04, 1.786041E+04, 1.946491E+04, 2.119405E+04,
2.305609E+04, 2.505975E+04, 2.721420E+04, 2.952910E+04, 3.201468E+04, 3.468164E+04,
3.754137E+04, 4.060572E+04, 4.388721E+04, 4.739912E+04, 5.115514E+04, 5.516994E+04,
5.945871E+04, 6.403752E+04, 6.892318E+04, 7.413333E+04, 7.968642E+04, 8.560184E+04,
9.189984E+04, 9.860168E+04, 1.057296E+05, 1.133067E+05, 1.213575E+05, 1.299074E+05,
1.389828E+05, 1.486115E+05, 1.588224E+05, 1.696459E+05, 1.811134E+05, 1.932580E+05,
2.061137E+05, 2.197165E+05, 2.341036E+05, 2.493138E+05, 2.653877E+05, 2.823672E+05,
3.002963E+05, 3.192205E+05, 3.391872E+05, 3.602457E+05, 3.824474E+05, 4.058456E+05,
4.304954E+05, 4.564546E+05, 4.837827E+05, 5.125418E+05, 5.427962E+05, 5.746128E+05,
6.080607E+05, 6.432115E+05, 6.801401E+05, 7.189233E+05, 7.596412E+05, 8.023769E+05,
8.472160E+05, 8.942474E+05, 9.435629E+05, 9.952583E+05, 1.049431E+06, 1.106185E+06,
1.165624E+06, 1.227858E+06, 1.292999E+06, 1.361164E+06, 1.432473E+06, 1.507052E+06,
1.585028E+06, 1.666535E+06, 1.751710E+06, 1.840694E+06, 1.933633E+06, 2.030680E+06,
2.131987E+06, 2.237718E+06, 2.348038E+06, 2.463117E+06, 2.583132E+06, 2.708264E+06,
2.838701E+06, 2.974635E+06, 3.116266E+06, 3.263798E+06, 3.417442E+06, 3.577415E+06,
3.743943E+06, 3.917253E+06, 4.097585E+06, 4.285180E+06, 4.480292E+06, 4.683179E+06,
4.894105E+06, 5.113345E+06, 5.341180E+06, 5.577898E+06, 5.823797E+06, 6.079183E+06,
6.344370E+06, 6.619679E+06, 6.905443E+06, 7.202001E+06, 7.509705E+06, 7.828915E+06,
8.159999E+06, 8.503333E+06, 8.859312E+06, 9.228330E+06, 9.610799E+06, 1.000714E+07,
1.041778E+07, 1.084317E+07, 1.128377E+07, 1.174002E+07, 1.221242E+07, 1.270146E+07,
1.320763E+07, 1.373146E+07, 1.427346E+07, 1.483419E+07, 1.541419E+07, 1.601404E+07,
1.663432E+07, 1.727563E+07, 1.793856E+07, 1.862376E+07, 1.933187E+07, 2.006354E+07,
2.081943E+07, 2.160025E+07, 2.240670E+07, 2.323950E+07, 2.409938E+07, 2.498711E+07,
2.590344E+07, 2.684920E+07,
])
# ---------------------- M = 27, I = 1 ---------------------------
M = 27
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.500304E+01, 9.183261E+02, 2.577299E+03, 4.726665E+03, 7.295077E+03, 1.027634E+04,
1.369384E+04, 1.758296E+04, 2.198535E+04, 2.695083E+04, 3.253906E+04, 3.882423E+04,
4.589599E+04, 5.386197E+04, 6.285216E+04, 7.301797E+04, 8.453841E+04, 9.762032E+04,
1.125046E+05, 1.294682E+05, 1.488302E+05, 1.709580E+05, 1.962724E+05, 2.252593E+05,
2.584720E+05, 2.965481E+05, 3.402164E+05, 3.903158E+05, 4.478043E+05, 5.137787E+05,
5.894956E+05, 6.763969E+05, 7.761243E+05, 8.905622E+05, 1.021863E+06, 1.172482E+06,
1.345229E+06, 1.543303E+06, 1.770360E+06, 2.030568E+06, 2.328675E+06, 2.670086E+06,
3.060957E+06, 3.508289E+06, 4.020046E+06, 4.605271E+06, 5.274240E+06, 6.038612E+06,
6.911616E+06, 7.908245E+06, 9.045487E+06, 1.034258E+07, 1.182128E+07, 1.350621E+07,
1.542519E+07, 1.760965E+07, 2.009505E+07, 2.292142E+07, 2.613387E+07, 2.978325E+07,
3.392680E+07, 3.862896E+07, 4.396218E+07, 5.000792E+07, 5.685769E+07, 6.461421E+07,
7.339274E+07, 8.332252E+07, 9.454840E+07, 1.072326E+08, 1.215567E+08, 1.377238E+08,
1.559610E+08, 1.765221E+08, 1.996904E+08, 2.257822E+08, 2.551502E+08, 2.881877E+08,
3.253328E+08, 3.670735E+08, 4.139525E+08, 4.665738E+08, 5.256087E+08, 5.918032E+08,
6.659852E+08, 7.490741E+08, 8.420894E+08, 9.461613E+08, 1.062542E+09, 1.192619E+09,
1.337927E+09, 1.500164E+09, 1.681208E+09, 1.883134E+09, 2.108234E+09, 2.359039E+09,
2.638341E+09, 2.949219E+09, 3.295067E+09, 3.679627E+09, 4.107016E+09, 4.581768E+09,
5.108871E+09, 5.693809E+09, 6.342612E+09, 7.061902E+09, 7.858951E+09, 8.741742E+09,
9.719033E+09, 1.080043E+10, 1.199645E+10, 1.331863E+10, 1.477960E+10, 1.639318E+10,
1.817449E+10, 2.014008E+10, 2.230804E+10, 2.469810E+10, 2.733187E+10, 3.023290E+10,
3.342690E+10, 3.694193E+10, 4.080858E+10, 4.506017E+10, 4.973304E+10, 5.486675E+10,
6.050436E+10, 6.669275E+10, 7.348290E+10, 8.093024E+10, 8.909503E+10, 9.804276E+10,
1.078445E+11, 1.185775E+11, 1.303256E+11, 1.431796E+11, 1.572382E+11, 1.726082E+11,
1.894054E+11, 2.077554E+11, 2.277941E+11, 2.496685E+11, 2.735379E+11, 2.995744E+11,
3.279644E+11, 3.589092E+11, 3.926263E+11, 4.293508E+11, 4.693366E+11, 5.128578E+11,
5.602101E+11, 6.117127E+11, 6.677096E+11, 7.285721E+11, 7.947001E+11, 8.665246E+11,
9.445098E+11, 1.029156E+12, 1.121000E+12, 1.220623E+12, 1.328646E+12, 1.445740E+12,
1.572626E+12, 1.710077E+12, 1.858927E+12, 2.020068E+12, 2.194462E+12, 2.383137E+12,
2.587201E+12, 2.807841E+12, 3.046329E+12, 3.304031E+12, 3.582410E+12, 3.883036E+12,
4.207589E+12, 4.557870E+12,
])
# ---------------------- M = 27, I = 2 ---------------------------
M = 27
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
7.614480E+00, 4.686721E+02, 1.315543E+03, 2.412801E+03, 3.724027E+03, 5.246064E+03,
6.990860E+03, 8.976483E+03, 1.122421E+04, 1.375949E+04, 1.661280E+04, 1.982202E+04,
2.343294E+04, 2.750053E+04, 3.209119E+04, 3.728225E+04, 4.316512E+04, 4.984547E+04,
5.744633E+04, 6.610916E+04, 7.599690E+04, 8.729731E+04, 1.002252E+05, 1.150289E+05,
1.319909E+05, 1.514370E+05, 1.737394E+05, 1.993267E+05, 2.286883E+05, 2.623844E+05,
3.010571E+05, 3.454428E+05, 3.963803E+05, 4.548324E+05, 5.218983E+05, 5.988332E+05,
6.870716E+05, 7.882488E+05, 9.042323E+05, 1.037152E+06, 1.189433E+06, 1.363837E+06,
1.563510E+06, 1.792029E+06, 2.053463E+06, 2.352434E+06, 2.694190E+06, 3.084692E+06,
3.530698E+06, 4.039869E+06, 4.620887E+06, 5.283581E+06, 6.039074E+06, 6.899946E+06,
7.880414E+06, 8.996537E+06, 1.026644E+07, 1.171059E+07, 1.335203E+07, 1.521675E+07,
1.733400E+07, 1.973673E+07, 2.246196E+07, 2.555133E+07, 2.905161E+07, 3.301531E+07,
3.750133E+07, 4.257574E+07, 4.831258E+07, 5.479478E+07, 6.211514E+07, 7.037750E+07,
7.969796E+07, 9.020622E+07, 1.020472E+08, 1.153824E+08, 1.303924E+08, 1.472781E+08,
1.662635E+08, 1.875980E+08, 2.115592E+08, 2.384560E+08, 2.686314E+08, 3.024668E+08,
3.403857E+08, 3.828581E+08, 4.304053E+08, 4.836051E+08, 5.430980E+08, 6.095931E+08,
6.838754E+08, 7.668134E+08, 8.593671E+08, 9.625978E+08, 1.077678E+09, 1.205900E+09,
1.348694E+09, 1.507634E+09, 1.684456E+09, 1.881072E+09, 2.099589E+09, 2.342326E+09,
2.611834E+09, 2.910918E+09, 3.242661E+09, 3.610451E+09, 4.018007E+09, 4.469413E+09,
4.969148E+09, 5.522123E+09, 6.133724E+09, 6.809848E+09, 7.556956E+09, 8.382119E+09,
9.293073E+09, 1.029828E+10, 1.140699E+10, 1.262932E+10, 1.397629E+10, 1.545998E+10,
1.709352E+10, 1.889128E+10, 2.086891E+10, 2.304345E+10, 2.543350E+10, 2.805929E+10,
3.094287E+10, 3.410821E+10, 3.758140E+10, 4.139081E+10, 4.556728E+10, 5.014429E+10,
5.515826E+10, 6.064867E+10, 6.665842E+10, 7.323403E+10, 8.042594E+10, 8.828887E+10,
9.688209E+10, 1.062698E+11, 1.165216E+11, 1.277127E+11, 1.399247E+11, 1.532456E+11,
1.677709E+11, 1.836035E+11, 2.008548E+11, 2.196451E+11, 2.401044E+11, 2.623730E+11,
2.866023E+11, 3.129555E+11, 3.416090E+11, 3.727526E+11, 4.065911E+11, 4.433452E+11,
4.832525E+11, 5.265688E+11, 5.735698E+11, 6.245518E+11, 6.798340E+11, 7.397592E+11,
8.046964E+11, 8.750421E+11, 9.512223E+11, 1.033695E+12, 1.122951E+12, 1.219518E+12,
1.323963E+12, 1.436894E+12, 1.558962E+12, 1.690867E+12, 1.833358E+12, 1.987238E+12,
2.153368E+12, 2.332671E+12,
])
# ---------------------- M = 28, I = 1 ---------------------------
M = 28
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[4]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
8.000210E+00, 6.080058E+01, 1.634756E+02, 2.965282E+02, 4.537635E+02, 6.319162E+02,
8.288067E+02, 1.042867E+03, 1.272974E+03, 1.518410E+03, 1.778850E+03, 2.054349E+03,
2.345319E+03, 2.652478E+03, 2.976809E+03, 3.319511E+03, 3.681956E+03, 4.065668E+03,
4.472291E+03, 4.903585E+03, 5.361415E+03, 5.847747E+03, 6.364650E+03, 6.914302E+03,
7.498988E+03, 8.121115E+03, 8.783208E+03, 9.487927E+03, 1.023807E+04, 1.103656E+04,
1.188652E+04, 1.279117E+04, 1.375395E+04, 1.477846E+04, 1.586846E+04, 1.702794E+04,
1.826107E+04, 1.957224E+04, 2.096604E+04, 2.244732E+04, 2.402115E+04, 2.569284E+04,
2.746798E+04, 2.935242E+04, 3.135229E+04, 3.347401E+04, 3.572430E+04, 3.811021E+04,
4.063911E+04, 4.331870E+04, 4.615704E+04, 4.916257E+04, 5.234409E+04, 5.571079E+04,
5.927230E+04, 6.303863E+04, 6.702025E+04, 7.122808E+04, 7.567351E+04, 8.036840E+04,
8.532511E+04, 9.055653E+04, 9.607607E+04, 1.018977E+05, 1.080359E+05, 1.145057E+05,
1.213230E+05, 1.285039E+05, 1.360654E+05, 1.440252E+05, 1.524013E+05, 1.612128E+05,
1.704793E+05, 1.802210E+05, 1.904590E+05, 2.012151E+05, 2.125118E+05, 2.243723E+05,
2.368209E+05, 2.498822E+05, 2.635820E+05, 2.779468E+05, 2.930039E+05, 3.087813E+05,
3.253080E+05, 3.426140E+05, 3.607297E+05, 3.796869E+05, 3.995178E+05, 4.202557E+05,
4.419347E+05, 4.645899E+05, 4.882572E+05, 5.129732E+05, 5.387755E+05, 5.657027E+05,
5.937941E+05, 6.230898E+05, 6.536308E+05, 6.854591E+05, 7.186173E+05, 7.531488E+05,
7.890981E+05, 8.265101E+05, 8.654309E+05, 9.059069E+05, 9.479856E+05, 9.917151E+05,
1.037144E+06, 1.084322E+06, 1.133300E+06, 1.184128E+06, 1.236857E+06, 1.291540E+06,
1.348229E+06, 1.406978E+06, 1.467840E+06, 1.530870E+06, 1.596122E+06, 1.663652E+06,
1.733515E+06, 1.805768E+06, 1.880466E+06, 1.957667E+06, 2.037427E+06, 2.119805E+06,
2.204858E+06, 2.292643E+06, 2.383220E+06, 2.476646E+06, 2.572979E+06, 2.672280E+06,
2.774606E+06, 2.880016E+06, 2.988569E+06, 3.100324E+06, 3.215340E+06, 3.333676E+06,
3.455390E+06, 3.580542E+06, 3.709190E+06, 3.841392E+06, 3.977207E+06, 4.116693E+06,
4.259908E+06, 4.406909E+06, 4.557755E+06, 4.712501E+06, 4.871206E+06, 5.033924E+06,
5.200714E+06, 5.371630E+06, 5.546727E+06, 5.726062E+06, 5.909687E+06, 6.097659E+06,
6.290029E+06, 6.486851E+06, 6.688177E+06, 6.894061E+06, 7.104553E+06, 7.319705E+06,
7.539566E+06, 7.764186E+06, 7.993616E+06, 8.227903E+06, 8.467096E+06, 8.711241E+06,
8.960386E+06, 9.214576E+06, 9.473858E+06, 9.738275E+06, 1.000787E+07, 1.028269E+07,
1.056277E+07, 1.084817E+07, 1.113891E+07, 1.143504E+07, 1.173659E+07, 1.204362E+07,
1.235615E+07, 1.267422E+07, 1.299787E+07, 1.332714E+07, 1.366206E+07, 1.400266E+07,
1.434898E+07, 1.470106E+07, 1.505891E+07, 1.542258E+07, 1.579209E+07, 1.616748E+07,
1.654877E+07, 1.693599E+07, 1.732917E+07, 1.772834E+07, 1.813351E+07, 1.854472E+07,
1.896199E+07, 1.938534E+07, 1.981480E+07, 2.025039E+07, 2.069212E+07, 2.114002E+07,
2.159411E+07, 2.205441E+07, 2.252093E+07, 2.299369E+07, 2.347271E+07, 2.395800E+07,
2.444958E+07, 2.494747E+07, 2.545167E+07, 2.596220E+07, 2.647907E+07, 2.700230E+07,
2.753188E+07, 2.806784E+07, 2.861019E+07, 2.915892E+07, 2.971405E+07, 3.027559E+07,
3.084354E+07, 3.141790E+07, 3.199869E+07, 3.258591E+07,
])
# ---------------------- M = 29, I = 1 ---------------------------
M = 29
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.300330E+01, 1.060780E+03, 2.994480E+03, 5.499119E+03, 8.466905E+03, 1.183885E+04,
1.558590E+04, 1.970577E+04, 2.422011E+04, 2.917030E+04, 3.461299E+04, 4.061669E+04,
4.725957E+04, 5.462826E+04, 6.281747E+04, 7.193003E+04, 8.207734E+04, 9.337996E+04,
1.059684E+05, 1.199837E+05, 1.355789E+05, 1.529192E+05, 1.721835E+05, 1.935653E+05,
2.172735E+05, 2.435340E+05, 2.725902E+05, 3.047047E+05, 3.401604E+05, 3.792616E+05,
4.223355E+05, 4.697336E+05, 5.218332E+05, 5.790385E+05, 6.417827E+05, 7.105294E+05,
7.857744E+05, 8.680471E+05, 9.579127E+05, 1.055974E+06, 1.162873E+06, 1.279295E+06,
1.405966E+06, 1.543660E+06, 1.693200E+06, 1.855457E+06, 2.031357E+06, 2.221881E+06,
2.428067E+06, 2.651016E+06, 2.891891E+06, 3.151923E+06, 3.432411E+06, 3.734728E+06,
4.060320E+06, 4.410715E+06, 4.787525E+06, 5.192441E+06, 5.627252E+06, 6.093834E+06,
6.594162E+06, 7.130313E+06, 7.704467E+06, 8.318917E+06, 8.976065E+06, 9.678431E+06,
1.042866E+07, 1.122953E+07, 1.208393E+07, 1.299491E+07, 1.396564E+07, 1.499946E+07,
1.609985E+07, 1.727043E+07, 1.851502E+07, 1.983759E+07, 2.124227E+07, 2.273339E+07,
2.431547E+07, 2.599322E+07, 2.777152E+07, 2.965551E+07, 3.165049E+07, 3.376202E+07,
3.599588E+07, 3.835806E+07, 4.085482E+07, 4.349265E+07, 4.627831E+07, 4.921882E+07,
5.232149E+07, 5.559387E+07, 5.904385E+07, 6.267958E+07, 6.650956E+07, 7.054254E+07,
7.478767E+07, 7.925441E+07, 8.395253E+07, 8.889220E+07, 9.408394E+07, 9.953866E+07,
1.052676E+08, 1.112825E+08, 1.175954E+08, 1.242189E+08, 1.311658E+08, 1.384496E+08,
1.460840E+08, 1.540834E+08, 1.624626E+08, 1.712367E+08, 1.804217E+08, 1.900338E+08,
2.000898E+08, 2.106070E+08, 2.216035E+08, 2.330976E+08, 2.451085E+08, 2.576558E+08,
2.707598E+08, 2.844415E+08, 2.987224E+08, 3.136247E+08, 3.291713E+08, 3.453860E+08,
3.622928E+08, 3.799169E+08, 3.982840E+08, 4.174208E+08, 4.373543E+08, 4.581129E+08,
4.797253E+08, 5.022216E+08, 5.256319E+08, 5.499880E+08, 5.753222E+08, 6.016678E+08,
6.290589E+08, 6.575307E+08, 6.871194E+08, 7.178619E+08, 7.497965E+08, 7.829623E+08,
8.173995E+08, 8.531493E+08, 8.902543E+08, 9.287578E+08, 9.687047E+08, 1.010141E+09,
1.053113E+09, 1.097669E+09, 1.143859E+09, 1.191734E+09, 1.241345E+09, 1.292747E+09,
1.345992E+09, 1.401139E+09, 1.458245E+09, 1.517367E+09, 1.578567E+09, 1.641907E+09,
1.707449E+09, 1.775259E+09, 1.845404E+09, 1.917950E+09, 1.992969E+09, 2.070532E+09,
2.150711E+09, 2.233580E+09, 2.319218E+09, 2.407703E+09, 2.499113E+09, 2.593533E+09,
2.691044E+09, 2.791734E+09,
])
# ---------------------- M = 29, I = 2 ---------------------------
M = 29
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
2.600659E+01, 2.121560E+03, 5.988960E+03, 1.099824E+04, 1.693381E+04, 2.367770E+04,
3.117187E+04, 3.941173E+04, 4.844066E+04, 5.834143E+04, 6.922734E+04, 8.123543E+04,
9.452204E+04, 1.092604E+05, 1.256400E+05, 1.438665E+05, 1.641626E+05, 1.867695E+05,
2.119482E+05, 2.399810E+05, 2.711735E+05, 3.058566E+05, 3.443879E+05, 3.871544E+05,
4.345740E+05, 4.870983E+05, 5.452144E+05, 6.094476E+05, 6.803632E+05, 7.585704E+05,
8.447233E+05, 9.395250E+05, 1.043730E+06, 1.158147E+06, 1.283642E+06, 1.421143E+06,
1.571641E+06, 1.736195E+06, 1.915934E+06, 2.112067E+06, 2.325876E+06, 2.558730E+06,
2.812083E+06, 3.087485E+06, 3.386577E+06, 3.711105E+06, 4.062920E+06, 4.443983E+06,
4.856372E+06, 5.302289E+06, 5.784058E+06, 6.304143E+06, 6.865140E+06, 7.469795E+06,
8.121003E+06, 8.821819E+06, 9.575464E+06, 1.038532E+07, 1.125497E+07, 1.218817E+07,
1.318886E+07, 1.426120E+07, 1.540954E+07, 1.663848E+07, 1.795281E+07, 1.935759E+07,
2.085809E+07, 2.245987E+07, 2.416872E+07, 2.599072E+07, 2.793224E+07, 2.999994E+07,
3.220077E+07, 3.454200E+07, 3.703124E+07, 3.967644E+07, 4.248586E+07, 4.546818E+07,
4.863242E+07, 5.198798E+07, 5.554466E+07, 5.931272E+07, 6.330278E+07, 6.752592E+07,
7.199374E+07, 7.671818E+07, 8.171180E+07, 8.698757E+07, 9.255900E+07, 9.844013E+07,
1.046456E+08, 1.111905E+08, 1.180905E+08, 1.253621E+08, 1.330222E+08, 1.410883E+08,
1.495787E+08, 1.585123E+08, 1.679087E+08, 1.777882E+08, 1.881719E+08, 1.990815E+08,
2.105395E+08, 2.225695E+08, 2.351955E+08, 2.484426E+08, 2.623366E+08, 2.769043E+08,
2.921733E+08, 3.081724E+08, 3.249309E+08, 3.424795E+08, 3.608497E+08, 3.800740E+08,
4.001862E+08, 4.212210E+08, 4.432141E+08, 4.662025E+08, 4.902245E+08, 5.153195E+08,
5.415277E+08, 5.688914E+08, 5.974535E+08, 6.272584E+08, 6.583520E+08, 6.907814E+08,
7.245953E+08, 7.598439E+08, 7.965784E+08, 8.348521E+08, 8.747197E+08, 9.162371E+08,
9.594623E+08, 1.004455E+09, 1.051276E+09, 1.099989E+09, 1.150657E+09, 1.203349E+09,
1.258132E+09, 1.315075E+09, 1.374253E+09, 1.435738E+09, 1.499608E+09, 1.565940E+09,
1.634815E+09, 1.706315E+09, 1.780525E+09, 1.857533E+09, 1.937427E+09, 2.020299E+09,
2.106243E+09, 2.195356E+09, 2.287737E+09, 2.383486E+09, 2.482710E+09, 2.585513E+09,
2.692005E+09, 2.802299E+09, 2.916510E+09, 3.034755E+09, 3.157156E+09, 3.283835E+09,
3.414921E+09, 3.550541E+09, 3.690830E+09, 3.835924E+09, 3.985962E+09, 4.141087E+09,
4.301445E+09, 4.467185E+09, 4.638461E+09, 4.815431E+09, 4.998252E+09, 5.187090E+09,
5.382113E+09, 5.583493E+09,
])
# ---------------------- M = 30, I = 1 ---------------------------
M = 30
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.021972E+02, 8.926883E+03, 2.522955E+04, 4.637336E+04, 7.178418E+04, 1.020099E+05,
1.386432E+05, 1.842557E+05, 2.424692E+05, 3.181910E+05, 4.180157E+05, 5.508060E+05,
7.284907E+05, 9.671311E+05, 1.288340E+06, 1.721151E+06, 2.304480E+06, 3.090346E+06,
4.148075E+06, 5.569754E+06, 7.477295E+06, 1.003153E+07, 1.344388E+07, 1.799125E+07,
2.403505E+07, 3.204519E+07, 4.263047E+07, 5.657681E+07, 7.489511E+07, 9.888099E+07,
1.301893E+08, 1.709260E+08, 2.237627E+08, 2.920765E+08, 3.801222E+08, 4.932437E+08,
6.381305E+08, 8.231307E+08, 1.058630E+09, 1.357512E+09, 1.735707E+09, 2.212863E+09,
2.813136E+09, 3.566151E+09, 4.508136E+09, 5.683276E+09, 7.145326E+09, 8.959512E+09,
1.120479E+10, 1.397648E+10, 1.738943E+10, 2.158165E+10, 2.671863E+10, 3.299836E+10,
4.065719E+10, 4.997670E+10, 6.129157E+10, 7.499890E+10, 9.156880E+10, 1.115568E+11,
1.356180E+11, 1.645237E+11, 1.991801E+11, 2.406500E+11, 2.901778E+11, 3.492179E+11,
4.194675E+11, 5.029034E+11, 6.018245E+11, 7.189005E+11, 8.572261E+11, 1.020383E+12,
1.212513E+12, 1.438393E+12, 1.703531E+12, 2.014262E+12, 2.377871E+12, 2.802713E+12,
3.298368E+12, 3.875798E+12, 4.547536E+12, 5.327887E+12, 6.233164E+12, 7.281945E+12,
8.495361E+12, 9.897418E+12, 1.151536E+13, 1.338007E+13, 1.552651E+13, 1.799421E+13,
2.082787E+13, 2.407789E+13, 2.780112E+13, 3.206156E+13, 3.693125E+13, 4.249113E+13,
4.883210E+13, 5.605613E+13, 6.427748E+13, 7.362411E+13, 8.423914E+13, 9.628255E+13,
1.099330E+14, 1.253896E+14, 1.428748E+14, 1.626357E+14, 1.849479E+14, 2.101173E+14,
2.384842E+14, 2.704260E+14, 3.063617E+14, 3.467555E+14, 3.921214E+14, 4.430285E+14,
5.001060E+14, 5.640492E+14, 6.356260E+14, 7.156838E+14, 8.051568E+14, 9.050747E+14,
1.016571E+15, 1.140894E+15, 1.279415E+15, 1.433643E+15, 1.605233E+15, 1.796004E+15,
2.007949E+15, 2.243255E+15, 2.504315E+15, 2.793752E+15, 3.114435E+15, 3.469502E+15,
3.862381E+15, 4.296819E+15, 4.776905E+15, 5.307101E+15, 5.892273E+15, 6.537723E+15,
7.249229E+15, 8.033080E+15, 8.896120E+15, 9.845792E+15, 1.089019E+16, 1.203810E+16,
1.329907E+16, 1.468347E+16, 1.620253E+16, 1.786845E+16, 1.969444E+16, 2.169481E+16,
2.388505E+16, 2.628195E+16, 2.890364E+16, 3.176975E+16, 3.490150E+16, 3.832178E+16,
4.205536E+16, 4.612893E+16, 5.057131E+16, 5.541360E+16, 6.068930E+16, 6.643451E+16,
7.268815E+16, 7.949209E+16, 8.689142E+16, 9.493463E+16, 1.036739E+17, 1.131653E+17,
1.234691E+17, 1.346500E+17, 1.467777E+17, 1.599265E+17, 1.741768E+17, 1.896144E+17,
2.063313E+17, 2.244263E+17,
])
# ---------------------- M = 31, I = 1 ---------------------------
M = 31
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[6]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.000000E+00, 9.577640E+00, 2.619804E+01, 4.723879E+01, 7.205446E+01, 1.001556E+02,
1.312023E+02, 1.649444E+02, 2.011901E+02, 2.397898E+02, 2.806276E+02, 3.236172E+02,
3.686976E+02, 4.158306E+02, 4.649971E+02, 5.161940E+02, 5.694318E+02, 6.247317E+02,
6.821239E+02, 7.416459E+02, 8.033414E+02, 8.672592E+02, 9.334532E+02, 1.001981E+03,
1.072904E+03, 1.146289E+03, 1.222203E+03, 1.300719E+03, 1.381913E+03, 1.465863E+03,
1.552650E+03, 1.642361E+03, 1.735081E+03, 1.830901E+03, 1.929915E+03, 2.032218E+03,
2.137907E+03, 2.247085E+03, 2.359854E+03, 2.476320E+03, 2.596589E+03, 2.720774E+03,
2.848985E+03, 2.981338E+03, 3.117948E+03, 3.258936E+03, 3.404422E+03, 3.554529E+03,
3.709382E+03, 3.869109E+03, 4.033838E+03, 4.203702E+03, 4.378833E+03, 4.559368E+03,
4.745444E+03, 4.937200E+03, 5.134779E+03, 5.338325E+03, 5.547982E+03, 5.763900E+03,
5.986229E+03, 6.215121E+03, 6.450730E+03, 6.693213E+03, 6.942728E+03, 7.199438E+03,
7.463503E+03, 7.735091E+03, 8.014367E+03, 8.301502E+03, 8.596667E+03, 8.900036E+03,
9.211786E+03, 9.532093E+03, 9.861140E+03, 1.019911E+04, 1.054618E+04, 1.090255E+04,
1.126840E+04, 1.164392E+04, 1.202931E+04, 1.242476E+04, 1.283047E+04, 1.324664E+04,
1.367348E+04, 1.411118E+04, 1.455995E+04, 1.502000E+04, 1.549154E+04, 1.597478E+04,
1.646995E+04, 1.697724E+04, 1.749688E+04, 1.802909E+04, 1.857410E+04, 1.913212E+04,
1.970338E+04, 2.028811E+04, 2.088653E+04, 2.149888E+04, 2.212539E+04, 2.276628E+04,
2.342180E+04, 2.409219E+04, 2.477767E+04, 2.547848E+04, 2.619488E+04, 2.692709E+04,
2.767536E+04, 2.843993E+04, 2.922104E+04, 3.001895E+04, 3.083389E+04, 3.166610E+04,
3.251585E+04, 3.338337E+04, 3.426891E+04, 3.517272E+04, 3.609505E+04, 3.703614E+04,
3.799625E+04, 3.897562E+04, 3.997449E+04, 4.099313E+04, 4.203177E+04, 4.309066E+04,
4.417005E+04, 4.527018E+04, 4.639131E+04, 4.753367E+04, 4.869751E+04, 4.988308E+04,
5.109061E+04, 5.232035E+04, 5.357253E+04, 5.484740E+04, 5.614520E+04, 5.746615E+04,
5.881050E+04, 6.017847E+04, 6.157031E+04, 6.298623E+04, 6.442647E+04, 6.589125E+04,
6.738080E+04, 6.889533E+04, 7.043508E+04, 7.200025E+04, 7.359107E+04, 7.520774E+04,
7.685048E+04, 7.851950E+04, 8.021500E+04, 8.193718E+04, 8.368625E+04, 8.546241E+04,
8.726584E+04, 8.909676E+04, 9.095533E+04, 9.284176E+04, 9.475623E+04, 9.669891E+04,
9.866999E+04, 1.006696E+05, 1.026980E+05, 1.047553E+05, 1.068417E+05, 1.089574E+05,
1.111024E+05, 1.132770E+05, 1.154813E+05, 1.177155E+05, 1.199797E+05, 1.222740E+05,
1.245986E+05, 1.269537E+05, 1.293393E+05, 1.317557E+05, 1.342028E+05, 1.366809E+05,
1.391901E+05, 1.417304E+05, 1.443021E+05, 1.469051E+05, 1.495397E+05, 1.522058E+05,
1.549037E+05, 1.576334E+05, 1.603950E+05, 1.631886E+05, 1.660143E+05, 1.688721E+05,
1.717622E+05, 1.746846E+05, 1.776393E+05, 1.806266E+05, 1.836463E+05, 1.866986E+05,
1.897836E+05, 1.929013E+05, 1.960517E+05,
])
# ---------------------- M = 31, I = 2 ---------------------------
M = 31
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.000000E+00, 9.858680E+00, 2.626126E+01, 4.730993E+01, 7.213596E+01, 1.002319E+02,
1.312533E+02, 1.649477E+02, 2.011172E+02, 2.396146E+02, 2.803171E+02, 3.231407E+02,
3.680172E+02, 4.149141E+02, 4.638055E+02, 5.146919E+02, 5.675764E+02, 6.224870E+02,
6.794483E+02, 7.384938E+02, 7.996744E+02, 8.630347E+02, 9.286157E+02, 9.964913E+02,
1.066714E+03, 1.139338E+03, 1.214443E+03, 1.292091E+03, 1.372359E+03, 1.455313E+03,
1.541049E+03, 1.629623E+03, 1.721154E+03, 1.815684E+03, 1.913338E+03, 2.014177E+03,
2.118315E+03, 2.225852E+03, 2.336870E+03, 2.451471E+03, 2.569761E+03, 2.691824E+03,
2.817792E+03, 2.947774E+03, 3.081835E+03, 3.220139E+03, 3.362775E+03, 3.509861E+03,
3.661488E+03, 3.817802E+03, 3.978929E+03, 4.144958E+03, 4.316016E+03, 4.492262E+03,
4.673789E+03, 4.860762E+03, 5.053242E+03, 5.251395E+03, 5.455392E+03, 5.665333E+03,
5.881313E+03, 6.103551E+03, 6.332104E+03, 6.567196E+03, 6.808930E+03, 7.057402E+03,
7.312850E+03, 7.575326E+03, 7.845073E+03, 8.122195E+03, 8.406841E+03, 8.699164E+03,
8.999319E+03, 9.307513E+03, 9.623851E+03, 9.948547E+03, 1.028170E+04, 1.062348E+04,
1.097410E+04, 1.133374E+04, 1.170255E+04, 1.208063E+04, 1.246830E+04, 1.286558E+04,
1.327280E+04, 1.369003E+04, 1.411755E+04, 1.455545E+04, 1.500391E+04, 1.546326E+04,
1.593352E+04, 1.641497E+04, 1.690779E+04, 1.741224E+04, 1.792835E+04, 1.845655E+04,
1.899688E+04, 1.954952E+04, 2.011484E+04, 2.069287E+04, 2.128398E+04, 2.188828E+04,
2.250597E+04, 2.313735E+04, 2.378263E+04, 2.444192E+04, 2.511562E+04, 2.580384E+04,
2.650680E+04, 2.722481E+04, 2.795798E+04, 2.870665E+04, 2.947101E+04, 3.025131E+04,
3.104787E+04, 3.186080E+04, 3.269035E+04, 3.353694E+04, 3.440060E+04, 3.528166E+04,
3.618048E+04, 3.709719E+04, 3.803214E+04, 3.898543E+04, 3.995757E+04, 4.094855E+04,
4.195886E+04, 4.298876E+04, 4.403836E+04, 4.510792E+04, 4.619795E+04, 4.730858E+04,
4.844006E+04, 4.959280E+04, 5.076704E+04, 5.196291E+04, 5.318083E+04, 5.442120E+04,
5.568414E+04, 5.697007E+04, 5.827913E+04, 5.961172E+04, 6.096828E+04, 6.234896E+04,
6.375399E+04, 6.518386E+04, 6.663882E+04, 6.811918E+04, 6.962521E+04, 7.115721E+04,
7.271564E+04, 7.430080E+04, 7.591297E+04, 7.755248E+04, 7.921976E+04, 8.091481E+04,
8.263842E+04, 8.439059E+04, 8.617196E+04, 8.798264E+04, 8.982298E+04, 9.169347E+04,
9.359443E+04, 9.552618E+04, 9.748903E+04, 9.948332E+04, 1.015096E+05, 1.035681E+05,
1.056590E+05, 1.077831E+05, 1.099405E+05, 1.121316E+05, 1.143570E+05, 1.166166E+05,
1.189111E+05, 1.212410E+05,
])
# ---------------------- M = 31, I = 3 ---------------------------
M = 31
I = 3
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
4.000000E+00, 3.938981E+01, 1.049203E+02, 1.890120E+02, 2.881945E+02, 4.004404E+02,
5.243733E+02, 6.589857E+02, 8.034856E+02, 9.572857E+02, 1.119896E+03, 1.290979E+03,
1.470264E+03, 1.657621E+03, 1.852946E+03, 2.056241E+03, 2.267518E+03, 2.486890E+03,
2.714455E+03, 2.950346E+03, 3.194767E+03, 3.447896E+03, 3.709897E+03, 3.981065E+03,
4.261609E+03, 4.551747E+03, 4.851797E+03, 5.162008E+03, 5.482685E+03, 5.814092E+03,
6.156613E+03, 6.510473E+03, 6.876144E+03, 7.253798E+03, 7.643934E+03, 8.046793E+03,
8.462831E+03, 8.892449E+03, 9.335972E+03, 9.793812E+03, 1.026639E+04, 1.075404E+04,
1.125729E+04, 1.177658E+04, 1.231216E+04, 1.286470E+04, 1.343454E+04, 1.402216E+04,
1.462791E+04, 1.525240E+04, 1.589611E+04, 1.655942E+04, 1.724280E+04, 1.794692E+04,
1.867213E+04, 1.941910E+04, 2.018807E+04, 2.097971E+04, 2.179469E+04, 2.263342E+04,
2.349628E+04, 2.438413E+04, 2.529722E+04, 2.623643E+04, 2.720217E+04, 2.819483E+04,
2.921536E+04, 3.026397E+04, 3.134163E+04, 3.244875E+04, 3.358594E+04, 3.475379E+04,
3.595293E+04, 3.718418E+04, 3.844797E+04, 3.974515E+04, 4.107614E+04, 4.244157E+04,
4.384233E+04, 4.527909E+04, 4.675250E+04, 4.826299E+04, 4.981175E+04, 5.139893E+04,
5.302576E+04, 5.469267E+04, 5.640061E+04, 5.815005E+04, 5.994168E+04, 6.177680E+04,
6.365556E+04, 6.557899E+04, 6.754782E+04, 6.956311E+04, 7.162501E+04, 7.373522E+04,
7.589386E+04, 7.810171E+04, 8.036020E+04, 8.266947E+04, 8.503099E+04, 8.744521E+04,
8.991295E+04, 9.243536E+04, 9.501329E+04, 9.764719E+04, 1.003387E+05, 1.030882E+05,
1.058965E+05, 1.087650E+05, 1.116941E+05, 1.146851E+05, 1.177388E+05, 1.208561E+05,
1.240384E+05, 1.272861E+05, 1.306002E+05, 1.339824E+05, 1.374328E+05, 1.409527E+05,
1.445436E+05, 1.482059E+05, 1.519411E+05, 1.557495E+05, 1.596333E+05, 1.635923E+05,
1.676286E+05, 1.717431E+05, 1.759363E+05, 1.802093E+05, 1.845641E+05, 1.890011E+05,
1.935214E+05, 1.981267E+05, 2.028178E+05, 2.075955E+05, 2.124611E+05, 2.174165E+05,
2.224620E+05, 2.275994E+05, 2.328292E+05, 2.381530E+05, 2.435725E+05, 2.490884E+05,
2.547017E+05, 2.604140E+05, 2.662267E+05, 2.721408E+05, 2.781576E+05, 2.842780E+05,
2.905041E+05, 2.968369E+05, 3.032776E+05, 3.098275E+05, 3.164884E+05, 3.232603E+05,
3.301462E+05, 3.371462E+05, 3.442629E+05, 3.514967E+05, 3.588490E+05, 3.663217E+05,
3.739162E+05, 3.816337E+05, 3.894754E+05, 3.974428E+05, 4.055377E+05, 4.137616E+05,
4.221151E+05, 4.306009E+05, 4.392197E+05, 4.479736E+05, 4.568640E+05, 4.658915E+05,
4.750581E+05, 4.843661E+05,
])
# ---------------------- M = 32, I = 1 ---------------------------
M = 32
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
8.098530E+00, 6.168274E+02, 1.738066E+03, 3.189875E+03, 4.909798E+03, 6.862997E+03,
9.030079E+03, 1.140495E+04, 1.399259E+04, 1.680815E+04, 1.987372E+04, 2.321812E+04,
2.687559E+04, 3.088517E+04, 3.529073E+04, 4.014075E+04, 4.548994E+04, 5.139632E+04,
5.792593E+04, 6.514813E+04, 7.314122E+04, 8.198985E+04, 9.178577E+04, 1.026295E+05,
1.146311E+05, 1.279089E+05, 1.425943E+05, 1.588277E+05, 1.767626E+05, 1.965672E+05,
2.184237E+05, 2.425277E+05, 2.690945E+05, 2.983565E+05, 3.305651E+05, 3.659933E+05,
4.049357E+05, 4.477128E+05, 4.946682E+05, 5.461776E+05, 6.026400E+05, 6.644928E+05,
7.322038E+05, 8.062787E+05, 8.872603E+05, 9.757342E+05, 1.072333E+06, 1.177728E+06,
1.292652E+06, 1.417886E+06, 1.554264E+06, 1.702691E+06, 1.864125E+06, 2.039604E+06,
2.230231E+06, 2.437189E+06, 2.661747E+06, 2.905255E+06, 3.169158E+06, 3.455004E+06,
3.764439E+06, 4.099224E+06, 4.461229E+06, 4.852460E+06, 5.275039E+06, 5.731238E+06,
6.223470E+06, 6.754297E+06, 7.326455E+06, 7.942838E+06, 8.606536E+06, 9.320818E+06,
1.008916E+07, 1.091524E+07, 1.180298E+07, 1.275651E+07, 1.378023E+07, 1.487879E+07,
1.605711E+07, 1.732042E+07, 1.867421E+07, 2.012433E+07, 2.167694E+07, 2.333855E+07,
2.511606E+07, 2.701671E+07, 2.904820E+07, 3.121862E+07, 3.353650E+07, 3.601086E+07,
3.865118E+07, 4.146747E+07, 4.447026E+07, 4.767066E+07, 5.108035E+07, 5.471163E+07,
5.857740E+07, 6.269129E+07, 6.706761E+07, 7.172137E+07, 7.666838E+07, 8.192520E+07,
8.750927E+07, 9.343889E+07, 9.973322E+07, 1.064124E+08, 1.134976E+08, 1.210109E+08,
1.289756E+08, 1.374159E+08, 1.463573E+08, 1.558267E+08, 1.658518E+08, 1.764620E+08,
1.876879E+08, 1.995616E+08, 2.121165E+08, 2.253878E+08, 2.394120E+08, 2.542275E+08,
2.698744E+08, 2.863944E+08, 3.038314E+08, 3.222307E+08, 3.416402E+08, 3.621095E+08,
3.836906E+08, 4.064374E+08, 4.304066E+08, 4.556569E+08, 4.822498E+08, 5.102492E+08,
5.397218E+08, 5.707372E+08, 6.033677E+08, 6.376886E+08, 6.737783E+08, 7.117187E+08,
7.515949E+08, 7.934953E+08, 8.375118E+08, 8.837404E+08, 9.322806E+08, 9.832362E+08,
1.036715E+09, 1.092828E+09, 1.151692E+09, 1.213428E+09, 1.278162E+09, 1.346024E+09,
1.417150E+09, 1.491679E+09, 1.569758E+09, 1.651538E+09, 1.737177E+09, 1.826838E+09,
1.920689E+09, 2.018907E+09, 2.121673E+09, 2.229176E+09, 2.341612E+09, 2.459182E+09,
2.582099E+09, 2.710579E+09, 2.844846E+09, 2.985137E+09, 3.131691E+09, 3.284760E+09,
3.444603E+09, 3.611489E+09, 3.785695E+09, 3.967509E+09, 4.157229E+09, 4.355164E+09,
4.561629E+09, 4.776956E+09,
])
# ---------------------- M = 33, I = 1 ---------------------------
M = 33
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
5.594200E-01, 7.688782E+01, 2.147062E+02, 3.927665E+02, 6.034216E+02, 8.422352E+02,
1.106209E+03, 1.393154E+03, 1.701421E+03, 2.029715E+03, 2.377155E+03, 2.743100E+03,
3.127307E+03, 3.529659E+03, 3.950367E+03, 4.389807E+03, 4.848487E+03, 5.327087E+03,
5.826359E+03, 6.347192E+03, 6.890410E+03, 7.456986E+03, 8.047900E+03, 8.664120E+03,
9.306654E+03, 9.976636E+03, 1.067496E+04, 1.140281E+04, 1.216121E+04, 1.295117E+04,
1.377383E+04, 1.463032E+04, 1.552167E+04, 1.644903E+04, 1.741356E+04, 1.841643E+04,
1.945864E+04, 2.054151E+04, 2.166621E+04, 2.283391E+04, 2.404598E+04, 2.530358E+04,
2.660771E+04, 2.796015E+04, 2.936189E+04, 3.081411E+04, 3.231868E+04, 3.387635E+04,
3.548880E+04, 3.715773E+04, 3.888414E+04, 4.066950E+04, 4.251530E+04, 4.442335E+04,
4.639489E+04, 4.843149E+04, 5.053503E+04, 5.270646E+04, 5.494802E+04, 5.726067E+04,
5.964671E+04, 6.210782E+04, 6.464494E+04, 6.726087E+04, 6.995658E+04, 7.273377E+04,
7.559498E+04, 7.854117E+04, 8.157495E+04, 8.469771E+04, 8.791125E+04, 9.121830E+04,
9.461985E+04, 9.811822E+04, 1.017153E+05, 1.054135E+05, 1.092148E+05, 1.131206E+05,
1.171339E+05, 1.212563E+05, 1.254897E+05, 1.298368E+05, 1.342996E+05, 1.388801E+05,
1.435812E+05, 1.484048E+05, 1.533526E+05, 1.584280E+05, 1.636325E+05, 1.689691E+05,
1.744392E+05, 1.800464E+05, 1.857931E+05, 1.916809E+05, 1.977128E+05, 2.038912E+05,
2.102190E+05, 2.166981E+05, 2.233322E+05, 2.301232E+05, 2.370735E+05, 2.441870E+05,
2.514648E+05, 2.589109E+05, 2.665280E+05, 2.743178E+05, 2.822846E+05, 2.904301E+05,
2.987578E+05, 3.072706E+05, 3.159718E+05, 3.248634E+05, 3.339491E+05, 3.432315E+05,
3.527136E+05, 3.623999E+05, 3.722915E+05, 3.823931E+05, 3.927067E+05, 4.032371E+05,
4.139854E+05, 4.249565E+05, 4.361534E+05, 4.475791E+05, 4.592368E+05, 4.711305E+05,
4.832632E+05, 4.956373E+05, 5.082588E+05, 5.211290E+05, 5.342531E+05, 5.476335E+05,
5.612732E+05, 5.751779E+05, 5.893485E+05, 6.037919E+05, 6.185091E+05, 6.335047E+05,
6.487834E+05, 6.643485E+05, 6.802023E+05, 6.963508E+05, 7.127974E+05, 7.295461E+05,
7.465986E+05, 7.639627E+05, 7.816395E+05, 7.996338E+05, 8.179508E+05, 8.365924E+05,
8.555657E+05, 8.748729E+05, 8.945175E+05, 9.145066E+05, 9.348407E+05, 9.555269E+05,
9.765689E+05, 9.979710E+05, 1.019738E+06, 1.041873E+06, 1.064382E+06, 1.087269E+06,
1.110538E+06, 1.134193E+06, 1.158240E+06, 1.182684E+06, 1.207529E+06, 1.232780E+06,
1.258440E+06, 1.284515E+06, 1.311010E+06, 1.337930E+06, 1.365279E+06, 1.393063E+06,
1.421286E+06, 1.449955E+06,
])
# ---------------------- M = 35, I = 1 ---------------------------
M = 35
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
2.408818E+02, 2.081987E+04, 5.959366E+04, 1.144363E+05, 1.889620E+05, 2.880202E+05,
4.176320E+05, 5.853070E+05, 8.004051E+05, 1.074538E+06, 1.422010E+06, 1.860338E+06,
2.410828E+06, 3.099228E+06, 3.956556E+06, 5.019996E+06, 6.333930E+06, 7.951208E+06,
9.934433E+06, 1.235765E+07, 1.530808E+07, 1.888813E+07, 2.321778E+07, 2.843710E+07,
3.470920E+07, 4.222351E+07, 5.119940E+07, 6.189045E+07, 7.458876E+07, 8.963031E+07,
1.074004E+08, 1.283401E+08, 1.529530E+08, 1.818133E+08, 2.155737E+08, 2.549750E+08,
3.008566E+08, 3.541675E+08, 4.159785E+08, 4.874961E+08, 5.700773E+08, 6.652451E+08,
7.747069E+08, 9.003730E+08, 1.044378E+09, 1.209104E+09, 1.397202E+09, 1.611625E+09,
1.855648E+09, 2.132908E+09, 2.447432E+09, 2.803673E+09, 3.206556E+09, 3.661513E+09,
4.174529E+09, 4.752195E+09, 5.401760E+09, 6.131182E+09, 6.949194E+09, 7.865367E+09,
8.890177E+09, 1.003509E+10, 1.131261E+10, 1.273641E+10, 1.432140E+10, 1.608379E+10,
1.804126E+10, 2.021301E+10, 2.261991E+10, 2.528461E+10, 2.823167E+10, 3.148772E+10,
3.508157E+10, 3.904440E+10, 4.340993E+10, 4.821455E+10, 5.349755E+10, 5.930134E+10,
6.567158E+10, 7.265748E+10, 8.031197E+10, 8.869200E+10, 9.785877E+10, 1.078780E+11,
1.188203E+11, 1.307613E+11, 1.437821E+11, 1.579696E+11, 1.734170E+11, 1.902238E+11,
2.084964E+11, 2.283487E+11, 2.499022E+11, 2.732868E+11, 2.986409E+11, 3.261123E+11,
3.558583E+11, 3.880465E+11, 4.228556E+11, 4.604755E+11, 5.011085E+11, 5.449694E+11,
5.922865E+11, 6.433023E+11, 6.982744E+11, 7.574760E+11, 8.211970E+11, 8.897447E+11,
9.634444E+11, 1.042642E+12, 1.127701E+12, 1.219010E+12, 1.316977E+12, 1.422034E+12,
1.534639E+12, 1.655275E+12, 1.784452E+12, 1.922709E+12, 2.070615E+12, 2.228770E+12,
2.397806E+12, 2.578390E+12, 2.771227E+12, 2.977056E+12, 3.196658E+12, 3.430854E+12,
3.680508E+12, 3.946531E+12, 4.229878E+12, 4.531554E+12, 4.852619E+12, 5.194181E+12,
5.557407E+12, 5.943522E+12, 6.353813E+12, 6.789628E+12, 7.252383E+12, 7.743565E+12,
8.264728E+12, 8.817504E+12, 9.403604E+12, 1.002482E+13, 1.068303E+13, 1.138019E+13,
1.211836E+13, 1.289970E+13, 1.372645E+13, 1.460097E+13, 1.552571E+13, 1.650326E+13,
1.753631E+13, 1.862765E+13, 1.978023E+13, 2.099711E+13, 2.228149E+13, 2.363672E+13,
2.506627E+13, 2.657379E+13, 2.816307E+13, 2.983806E+13, 3.160289E+13, 3.346185E+13,
3.541944E+13, 3.748031E+13, 3.964933E+13, 4.193156E+13, 4.433228E+13, 4.685697E+13,
4.951135E+13, 5.230137E+13, 5.523320E+13, 5.831329E+13, 6.154832E+13, 6.494525E+13,
6.851131E+13, 7.225402E+13,
])
# ---------------------- M = 35, I = 2 ---------------------------
M = 35
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
2.468098E+02, 2.134900E+04, 6.110945E+04, 1.173478E+05, 1.937703E+05, 2.953497E+05,
4.282605E+05, 6.002031E+05, 8.207761E+05, 1.101887E+06, 1.458203E+06, 1.907688E+06,
2.472190E+06, 3.178113E+06, 4.057263E+06, 5.147772E+06, 6.495151E+06, 8.153595E+06,
1.018730E+07, 1.267221E+07, 1.569774E+07, 1.936891E+07, 2.380877E+07, 2.916094E+07,
3.559270E+07, 4.329828E+07, 5.250266E+07, 6.346585E+07, 7.648739E+07, 9.191183E+07,
1.101343E+08, 1.316070E+08, 1.568465E+08, 1.864414E+08, 2.210611E+08, 2.614653E+08,
3.085149E+08, 3.631828E+08, 4.265672E+08, 4.999054E+08, 5.845887E+08, 6.821789E+08,
7.944272E+08, 9.232921E+08, 1.070963E+09, 1.239882E+09, 1.432769E+09, 1.652649E+09,
1.902884E+09, 2.187202E+09, 2.509732E+09, 2.875042E+09, 3.288180E+09, 3.754718E+09,
4.280793E+09, 4.873164E+09, 5.539263E+09, 6.287254E+09, 7.126088E+09, 8.065583E+09,
9.116481E+09, 1.029053E+10, 1.160058E+10, 1.306063E+10, 1.468596E+10, 1.649322E+10,
1.850051E+10, 2.072754E+10, 2.319571E+10, 2.592825E+10, 2.895033E+10, 3.228926E+10,
3.597459E+10, 4.003830E+10, 4.451495E+10, 4.944187E+10, 5.485937E+10, 6.081089E+10,
6.734329E+10, 7.450702E+10, 8.235636E+10, 9.094971E+10, 1.003498E+11, 1.106241E+11,
1.218449E+11, 1.340899E+11, 1.474421E+11, 1.619909E+11, 1.778314E+11, 1.950660E+11,
2.138038E+11, 2.341615E+11, 2.562637E+11, 2.802435E+11, 3.062431E+11, 3.344137E+11,
3.649168E+11, 3.979245E+11, 4.336196E+11, 4.721973E+11, 5.138646E+11, 5.588420E+11,
6.073635E+11, 6.596780E+11, 7.160494E+11, 7.767581E+11, 8.421012E+11, 9.123937E+11,
9.879697E+11, 1.069183E+12, 1.156408E+12, 1.250041E+12, 1.350501E+12, 1.458233E+12,
1.573704E+12, 1.697411E+12, 1.829877E+12, 1.971654E+12, 2.123324E+12, 2.285505E+12,
2.458844E+12, 2.644025E+12, 2.841771E+12, 3.052839E+12, 3.278031E+12, 3.518189E+12,
3.774199E+12, 4.046993E+12, 4.337553E+12, 4.646909E+12, 4.976146E+12, 5.326403E+12,
5.698874E+12, 6.094819E+12, 6.515555E+12, 6.962464E+12, 7.436999E+12, 7.940684E+12,
8.475113E+12, 9.041962E+12, 9.642982E+12, 1.028001E+13, 1.095497E+13, 1.166988E+13,
1.242685E+13, 1.322807E+13, 1.407587E+13, 1.497265E+13, 1.592093E+13, 1.692337E+13,
1.798271E+13, 1.910183E+13, 2.028375E+13, 2.153161E+13, 2.284869E+13, 2.423842E+13,
2.570436E+13, 2.725025E+13, 2.887999E+13, 3.059761E+13, 3.240737E+13, 3.431365E+13,
3.632107E+13, 3.843441E+13, 4.065863E+13, 4.299897E+13, 4.546079E+13, 4.804976E+13,
5.077171E+13, 5.363275E+13, 5.663921E+13, 5.979770E+13, 6.311508E+13, 6.659849E+13,
7.025532E+13, 7.409331E+13,
])
# ---------------------- M = 36, I = 1 ---------------------------
M = 36
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
3.029540E+00, 2.200969E+01, 4.297675E+01, 6.395552E+01, 8.493845E+01, 1.059240E+02,
1.269117E+02, 1.479015E+02, 1.688930E+02, 1.898864E+02, 2.108815E+02, 2.318784E+02,
2.528769E+02, 2.738772E+02, 2.948822E+02, 3.158861E+02, 3.368985E+02, 3.579134E+02,
3.789385E+02, 3.999708E+02, 4.210197E+02, 4.420868E+02, 4.631741E+02, 4.842933E+02,
5.054422E+02, 5.266341E+02, 5.478727E+02, 5.691675E+02, 5.905169E+02, 6.119432E+02,
6.334393E+02, 6.550162E+02, 6.766925E+02, 6.984603E+02, 7.203322E+02, 7.423211E+02,
7.644255E+02, 7.866515E+02, 8.090126E+02, 8.315068E+02, 8.541484E+02, 8.769350E+02,
8.998723E+02, 9.229750E+02, 9.462310E+02, 9.696643E+02, 9.932616E+02, 1.017048E+03,
1.041000E+03, 1.065141E+03, 1.089479E+03, 1.113997E+03, 1.138710E+03, 1.163612E+03,
1.188731E+03, 1.214038E+03, 1.239547E+03, 1.265277E+03, 1.291206E+03, 1.317352E+03,
1.343706E+03, 1.370272E+03, 1.397067E+03, 1.424069E+03, 1.451294E+03, 1.478747E+03,
1.506416E+03, 1.534307E+03, 1.562435E+03, 1.590776E+03, 1.619347E+03, 1.648150E+03,
1.677191E+03, 1.706455E+03, 1.735962E+03, 1.765682E+03, 1.795650E+03, 1.825853E+03,
1.856277E+03, 1.886958E+03, 1.917863E+03, 1.949013E+03, 1.980393E+03, 2.012022E+03,
2.043868E+03, 2.075985E+03, 2.108322E+03, 2.140900E+03, 2.173719E+03, 2.206782E+03,
2.240090E+03, 2.273646E+03, 2.307451E+03, 2.341488E+03, 2.375759E+03, 2.410284E+03,
2.445065E+03, 2.480064E+03, 2.515321E+03, 2.550840E+03, 2.586581E+03, 2.622585E+03,
2.658813E+03, 2.695308E+03, 2.732051E+03, 2.769042E+03, 2.806260E+03, 2.843730E+03,
2.881473E+03, 2.919448E+03, 2.957655E+03, 2.996140E+03, 3.034859E+03, 3.073813E+03,
3.113025E+03, 3.152497E+03, 3.192207E+03, 3.232179E+03, 3.272390E+03, 3.312840E+03,
3.353556E+03, 3.394513E+03, 3.435739E+03, 3.477180E+03, 3.518892E+03, 3.560874E+03,
3.603075E+03, 3.645548E+03, 3.688268E+03, 3.731208E+03, 3.774424E+03, 3.817915E+03,
3.861629E+03, 3.905593E+03, 3.949807E+03, 3.994273E+03, 4.039020E+03, 4.083990E+03,
4.129214E+03, 4.174693E+03, 4.220425E+03, 4.266414E+03, 4.312628E+03, 4.359128E+03,
4.405856E+03, 4.452842E+03, 4.500085E+03, 4.547587E+03, 4.595349E+03, 4.643340E+03,
4.691592E+03, 4.740104E+03, 4.788878E+03, 4.837883E+03, 4.887149E+03, 4.936647E+03,
4.986441E+03, 5.036467E+03, 5.086725E+03, 5.137247E+03, 5.188036E+03, 5.239091E+03,
5.290379E+03, 5.341935E+03, 5.393723E+03, 5.445780E+03, 5.498071E+03, 5.550631E+03,
5.603462E+03, 5.656527E+03, 5.709827E+03, 5.763397E+03, 5.817240E+03, 5.871319E+03,
5.925670E+03, 5.980257E+03,
])
# ---------------------- M = 37, I = 1 ---------------------------
M = 37
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
5.785130E+00, 4.726760E+02, 1.331909E+03, 2.444466E+03, 3.762449E+03, 5.258748E+03,
6.916853E+03, 8.727864E+03, 1.068858E+04, 1.280031E+04, 1.506728E+04, 1.749553E+04,
2.009268E+04, 2.286748E+04, 2.582902E+04, 2.898719E+04, 3.235219E+04, 3.593458E+04,
3.974451E+04, 4.379322E+04, 4.809173E+04, 5.265152E+04, 5.748313E+04, 6.259898E+04,
6.800999E+04, 7.372804E+04, 7.976499E+04, 8.613334E+04, 9.284498E+04, 9.991164E+04,
1.073465E+05, 1.151622E+05, 1.233719E+05, 1.319883E+05, 1.410250E+05, 1.504953E+05,
1.604130E+05, 1.707918E+05, 1.816466E+05, 1.929925E+05, 2.048425E+05, 2.172130E+05,
2.301186E+05, 2.435762E+05, 2.576005E+05, 2.722077E+05, 2.874142E+05, 3.032379E+05,
3.196943E+05, 3.368020E+05, 3.545764E+05, 3.730385E+05, 3.922061E+05, 4.120955E+05,
4.327284E+05, 4.541215E+05, 4.762955E+05, 4.992694E+05, 5.230665E+05, 5.477044E+05,
5.732050E+05, 5.995885E+05, 6.268775E+05, 6.550925E+05, 6.842570E+05, 7.143946E+05,
7.455270E+05, 7.776759E+05, 8.108659E+05, 8.451225E+05, 8.804679E+05, 9.169282E+05,
9.545270E+05, 9.932902E+05, 1.033245E+06, 1.074416E+06, 1.116828E+06, 1.160511E+06,
1.205491E+06, 1.251794E+06, 1.299452E+06, 1.348487E+06, 1.398931E+06, 1.450812E+06,
1.504163E+06, 1.559011E+06, 1.615386E+06, 1.673318E+06, 1.732838E+06, 1.793976E+06,
1.856768E+06, 1.921245E+06, 1.987436E+06, 2.055375E+06, 2.125098E+06, 2.196636E+06,
2.270020E+06, 2.345293E+06, 2.422481E+06, 2.501629E+06, 2.582761E+06, 2.665923E+06,
2.751144E+06, 2.838463E+06, 2.927919E+06, 3.019552E+06, 3.113392E+06, 3.209490E+06,
3.307870E+06, 3.408585E+06, 3.511666E+06, 3.617155E+06, 3.725094E+06, 3.835528E+06,
3.948487E+06, 4.064024E+06, 4.182175E+06, 4.302986E+06, 4.426502E+06, 4.552764E+06,
4.681813E+06, 4.813699E+06, 4.948460E+06, 5.086155E+06, 5.226813E+06, 5.370497E+06,
5.517235E+06, 5.667093E+06, 5.820104E+06, 5.976328E+06, 6.135809E+06, 6.298591E+06,
6.464727E+06, 6.634271E+06, 6.807274E+06, 6.983775E+06, 7.163838E+06, 7.347517E+06,
7.534852E+06, 7.725898E+06, 7.920720E+06, 8.119366E+06, 8.321879E+06, 8.528331E+06,
8.738766E+06, 8.953250E+06, 9.171827E+06, 9.394565E+06, 9.621507E+06, 9.852731E+06,
1.008828E+07, 1.032821E+07, 1.057259E+07, 1.082148E+07, 1.107494E+07, 1.133303E+07,
1.159581E+07, 1.186333E+07, 1.213567E+07, 1.241289E+07, 1.269505E+07, 1.298221E+07,
1.327444E+07, 1.357180E+07, 1.387436E+07, 1.418219E+07, 1.449535E+07, 1.481390E+07,
1.513792E+07, 1.546748E+07, 1.580263E+07, 1.614346E+07, 1.649004E+07, 1.684241E+07,
1.720068E+07, 1.756490E+07,
])
# ---------------------- M = 37, I = 2 ---------------------------
M = 37
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
5.739990E+00, 4.708308E+02, 1.326733E+03, 2.434981E+03, 3.747861E+03, 5.238419E+03,
6.890124E+03, 8.694231E+03, 1.064761E+04, 1.275163E+04, 1.501028E+04, 1.743002E+04,
2.001840E+04, 2.278379E+04, 2.573568E+04, 2.888372E+04, 3.223779E+04, 3.580868E+04,
3.960721E+04, 4.364360E+04, 4.792918E+04, 5.247539E+04, 5.729323E+04, 6.239415E+04,
6.778948E+04, 7.349165E+04, 7.951198E+04, 8.586227E+04, 9.255496E+04, 9.960247E+04,
1.070173E+05, 1.148120E+05, 1.229998E+05, 1.315924E+05, 1.406050E+05, 1.500502E+05,
1.599417E+05, 1.702941E+05, 1.811203E+05, 1.924363E+05, 2.042563E+05, 2.165956E+05,
2.294686E+05, 2.428909E+05, 2.568798E+05, 2.714501E+05, 2.866181E+05, 3.024019E+05,
3.188166E+05, 3.358826E+05, 3.536137E+05, 3.720290E+05, 3.911480E+05, 4.109904E+05,
4.315709E+05, 4.529114E+05, 4.750329E+05, 4.979522E+05, 5.216906E+05, 5.462676E+05,
5.717072E+05, 5.980275E+05, 6.252486E+05, 6.533981E+05, 6.824923E+05, 7.125570E+05,
7.436139E+05, 7.756873E+05, 8.087991E+05, 8.429747E+05, 8.782365E+05, 9.146103E+05,
9.521193E+05, 9.907930E+05, 1.030652E+06, 1.071728E+06, 1.114040E+06, 1.157621E+06,
1.202496E+06, 1.248693E+06, 1.296235E+06, 1.345157E+06, 1.395485E+06, 1.447246E+06,
1.500473E+06, 1.555196E+06, 1.611440E+06, 1.669235E+06, 1.728620E+06, 1.789621E+06,
1.852269E+06, 1.916593E+06, 1.982635E+06, 2.050416E+06, 2.119981E+06, 2.191356E+06,
2.264576E+06, 2.339672E+06, 2.416687E+06, 2.495652E+06, 2.576605E+06, 2.659573E+06,
2.744601E+06, 2.831725E+06, 2.920982E+06, 3.012404E+06, 3.106038E+06, 3.201913E+06,
3.300075E+06, 3.400561E+06, 3.503412E+06, 3.608665E+06, 3.716362E+06, 3.826546E+06,
3.939250E+06, 4.054530E+06, 4.172418E+06, 4.292959E+06, 4.416198E+06, 4.542175E+06,
4.670937E+06, 4.802531E+06, 4.936999E+06, 5.074379E+06, 5.214727E+06, 5.358088E+06,
5.504508E+06, 5.654031E+06, 5.806702E+06, 5.962583E+06, 6.121705E+06, 6.284127E+06,
6.449902E+06, 6.619068E+06, 6.791685E+06, 6.967806E+06, 7.147471E+06, 7.330735E+06,
7.517662E+06, 7.708297E+06, 7.902682E+06, 8.100891E+06, 8.302962E+06, 8.508957E+06,
8.718940E+06, 8.932943E+06, 9.151047E+06, 9.373291E+06, 9.599747E+06, 9.830455E+06,
1.006549E+07, 1.030491E+07, 1.054875E+07, 1.079710E+07, 1.105000E+07, 1.130753E+07,
1.156973E+07, 1.183667E+07, 1.210843E+07, 1.238504E+07, 1.266658E+07, 1.295312E+07,
1.324472E+07, 1.354143E+07, 1.384333E+07, 1.415049E+07, 1.446297E+07, 1.478083E+07,
1.510415E+07, 1.543299E+07, 1.576743E+07, 1.610752E+07, 1.645334E+07, 1.680496E+07,
1.716245E+07, 1.752589E+07,
])
# ---------------------- M = 38, I = 1 ---------------------------
M = 38
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
2.964040E+00, 1.867813E+02, 5.232315E+02, 9.584283E+02, 1.473741E+03, 2.058415E+03,
2.705341E+03, 3.409831E+03, 4.169420E+03, 4.983909E+03, 5.855633E+03, 6.789060E+03,
7.791043E+03, 8.870171E+03, 1.003696E+04, 1.130344E+04, 1.268335E+04, 1.419187E+04,
1.584565E+04, 1.766292E+04, 1.966394E+04, 2.187063E+04, 2.430711E+04, 2.699984E+04,
2.997740E+04, 3.327167E+04, 3.691750E+04, 4.095290E+04, 4.541954E+04, 5.036362E+04,
5.583538E+04, 6.189039E+04, 6.858858E+04, 7.599728E+04, 8.418919E+04, 9.324438E+04,
1.032502E+05, 1.143029E+05, 1.265073E+05, 1.399784E+05, 1.548424E+05, 1.712363E+05,
1.893101E+05, 2.092280E+05, 2.311692E+05, 2.553286E+05, 2.819194E+05, 3.111737E+05,
3.433445E+05, 3.787068E+05, 4.175601E+05, 4.602299E+05, 5.070709E+05, 5.584681E+05,
6.148394E+05, 6.766383E+05, 7.443566E+05, 8.185285E+05, 8.997333E+05, 9.885977E+05,
1.085799E+06, 1.192073E+06, 1.308215E+06, 1.435085E+06, 1.573609E+06, 1.724794E+06,
1.889722E+06, 2.069561E+06, 2.265577E+06, 2.479128E+06, 2.711680E+06, 2.964814E+06,
3.240234E+06, 3.539768E+06, 3.865391E+06, 4.219220E+06, 4.603536E+06, 5.020787E+06,
5.473605E+06, 5.964816E+06, 6.497450E+06, 7.074761E+06, 7.700237E+06, 8.377621E+06,
9.110918E+06, 9.904421E+06, 1.076273E+07, 1.169076E+07, 1.269377E+07, 1.377741E+07,
1.494767E+07, 1.621101E+07, 1.757429E+07, 1.904485E+07, 2.063052E+07, 2.233968E+07,
2.418123E+07, 2.616470E+07, 2.830021E+07, 3.059859E+07, 3.307135E+07, 3.573073E+07,
3.858981E+07, 4.166247E+07, 4.496350E+07, 4.850860E+07, 5.231451E+07, 5.639898E+07,
6.078088E+07, 6.548028E+07, 7.051845E+07, 7.591801E+07, 8.170292E+07, 8.789865E+07,
9.453217E+07, 1.016321E+08, 1.092288E+08, 1.173543E+08, 1.260428E+08, 1.353302E+08,
1.452548E+08, 1.558569E+08, 1.671794E+08, 1.792673E+08, 1.921686E+08, 2.059338E+08,
2.206164E+08, 2.362728E+08, 2.529626E+08, 2.707490E+08, 2.896984E+08, 3.098811E+08,
3.313711E+08, 3.542468E+08, 3.785905E+08, 4.044891E+08, 4.320345E+08, 4.613233E+08,
4.924573E+08, 5.255437E+08, 5.606957E+08, 5.980320E+08, 6.376782E+08, 6.797660E+08,
7.244340E+08, 7.718283E+08, 8.221023E+08, 8.754174E+08, 9.319435E+08, 9.918586E+08,
1.055351E+09, 1.122616E+09, 1.193862E+09, 1.269305E+09, 1.349174E+09, 1.433708E+09,
1.523159E+09, 1.617789E+09, 1.717876E+09, 1.823709E+09, 1.935593E+09, 2.053846E+09,
2.178804E+09, 2.310816E+09, 2.450248E+09, 2.597487E+09, 2.752934E+09, 2.917011E+09,
3.090161E+09, 3.272845E+09, 3.465547E+09, 3.668772E+09, 3.883052E+09, 4.108941E+09,
4.347015E+09, 4.597885E+09,
])
# ---------------------- M = 38, I = 2 ---------------------------
M = 38
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.204615E+01, 7.642634E+02, 2.141386E+03, 3.922752E+03, 6.032083E+03, 8.425355E+03,
1.107346E+04, 1.395721E+04, 1.706650E+04, 2.040053E+04, 2.396885E+04, 2.778974E+04,
3.189127E+04, 3.630859E+04, 4.108475E+04, 4.626896E+04, 5.191754E+04, 5.809252E+04,
6.486214E+04, 7.230097E+04, 8.049201E+04, 8.952494E+04, 9.949845E+04, 1.105209E+05,
1.227093E+05, 1.361942E+05, 1.511181E+05, 1.676367E+05, 1.859206E+05, 2.061588E+05,
2.285571E+05, 2.533428E+05, 2.807614E+05, 3.110884E+05, 3.446214E+05, 3.816882E+05,
4.226462E+05, 4.678897E+05, 5.178476E+05, 5.729908E+05, 6.338355E+05, 7.009427E+05,
7.749268E+05, 8.564597E+05, 9.462743E+05, 1.045169E+06, 1.154017E+06, 1.273768E+06,
1.405457E+06, 1.550210E+06, 1.709254E+06, 1.883920E+06, 2.075661E+06, 2.286052E+06,
2.516805E+06, 2.769775E+06, 3.046976E+06, 3.350594E+06, 3.683001E+06, 4.046763E+06,
4.444651E+06, 4.879678E+06, 5.355099E+06, 5.874432E+06, 6.441474E+06, 7.060342E+06,
7.735464E+06, 8.471627E+06, 9.274007E+06, 1.014816E+07, 1.110010E+07, 1.213630E+07,
1.326371E+07, 1.448984E+07, 1.582276E+07, 1.727114E+07, 1.884431E+07, 2.055231E+07,
2.240589E+07, 2.441664E+07, 2.659695E+07, 2.896014E+07, 3.152050E+07, 3.429333E+07,
3.729505E+07, 4.054321E+07, 4.405664E+07, 4.785548E+07, 5.196127E+07, 5.639707E+07,
6.118750E+07, 6.635891E+07, 7.193943E+07, 7.795909E+07, 8.444997E+07, 9.144632E+07,
9.898462E+07, 1.071038E+08, 1.158455E+08, 1.252538E+08, 1.353759E+08, 1.462619E+08,
1.579654E+08, 1.705432E+08, 1.840558E+08, 1.985675E+08, 2.141468E+08, 2.308664E+08,
2.488035E+08, 2.680402E+08, 2.886638E+08, 3.107666E+08, 3.344469E+08, 3.598088E+08,
3.869628E+08, 4.160260E+08, 4.471227E+08, 4.803842E+08, 5.159500E+08, 5.539677E+08,
5.945935E+08, 6.379928E+08, 6.843406E+08, 7.338221E+08, 7.866329E+08, 8.429801E+08,
9.030826E+08, 9.671712E+08, 1.035490E+09, 1.108298E+09, 1.185867E+09, 1.268484E+09,
1.356452E+09, 1.450092E+09, 1.549742E+09, 1.655757E+09, 1.768513E+09, 1.888406E+09,
2.015851E+09, 2.151289E+09, 2.295182E+09, 2.448017E+09, 2.610306E+09, 2.782591E+09,
2.965437E+09, 3.159443E+09, 3.365238E+09, 3.583481E+09, 3.814868E+09, 4.060128E+09,
4.320030E+09, 4.595378E+09, 4.887019E+09, 5.195844E+09, 5.522783E+09, 5.868820E+09,
6.234981E+09, 6.622346E+09, 7.032047E+09, 7.465271E+09, 7.923262E+09, 8.407328E+09,
8.918836E+09, 9.459220E+09, 1.002998E+10, 1.063270E+10, 1.126901E+10, 1.194066E+10,
1.264944E+10, 1.339724E+10, 1.418606E+10, 1.501796E+10, 1.589510E+10, 1.681977E+10,
1.779432E+10, 1.882124E+10,
])
# ---------------------- M = 39, I = 1 ---------------------------
M = 39
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.441130E+01, 8.984785E+02, 2.519098E+03, 4.622384E+03, 7.153276E+03, 1.012212E+04,
1.356235E+04, 1.751151E+04, 2.200662E+04, 2.708504E+04, 3.278708E+04, 3.915884E+04,
4.625465E+04, 5.413906E+04, 6.288836E+04, 7.259164E+04, 8.335181E+04, 9.528638E+04,
1.085284E+05, 1.232272E+05, 1.395500E+05, 1.576826E+05, 1.778316E+05, 2.002254E+05,
2.251168E+05, 2.527848E+05, 2.835372E+05, 3.177134E+05, 3.556871E+05, 3.978696E+05,
4.447142E+05, 4.967184E+05, 5.544300E+05, 6.184503E+05, 6.894398E+05, 7.681233E+05,
8.552960E+05, 9.518298E+05, 1.058680E+06, 1.176894E+06, 1.307616E+06, 1.452099E+06,
1.611715E+06, 1.787960E+06, 1.982471E+06, 2.197034E+06, 2.433598E+06, 2.694287E+06,
2.981419E+06, 3.297516E+06, 3.645326E+06, 4.027840E+06, 4.448308E+06, 4.910266E+06,
5.417556E+06, 5.974349E+06, 6.585171E+06, 7.254935E+06, 7.988968E+06, 8.793039E+06,
9.673404E+06, 1.063683E+07, 1.169064E+07, 1.284277E+07, 1.410177E+07, 1.547691E+07,
1.697818E+07, 1.861638E+07, 2.040317E+07, 2.235109E+07, 2.447370E+07, 2.678560E+07,
2.930251E+07, 3.204136E+07, 3.502035E+07, 3.825912E+07, 4.177870E+07, 4.560178E+07,
4.975264E+07, 5.425747E+07, 5.914429E+07, 6.444319E+07, 7.018647E+07, 7.640872E+07,
8.314707E+07, 9.044122E+07, 9.833376E+07, 1.068702E+08, 1.160993E+08, 1.260732E+08,
1.368476E+08, 1.484821E+08, 1.610404E+08, 1.745904E+08, 1.892048E+08, 2.049609E+08,
2.219414E+08, 2.402345E+08, 2.599341E+08, 2.811403E+08, 3.039598E+08, 3.285064E+08,
3.549009E+08, 3.832720E+08, 4.137567E+08, 4.465008E+08, 4.816591E+08, 5.193963E+08,
5.598872E+08, 6.033178E+08, 6.498853E+08, 6.997992E+08, 7.532821E+08, 8.105694E+08,
8.719115E+08, 9.375733E+08, 1.007836E+09, 1.082998E+09, 1.163375E+09, 1.249300E+09,
1.341128E+09, 1.439233E+09, 1.544011E+09, 1.655881E+09, 1.775287E+09, 1.902698E+09,
2.038608E+09, 2.183542E+09, 2.338052E+09, 2.502722E+09, 2.678169E+09, 2.865044E+09,
3.064034E+09, 3.275863E+09, 3.501296E+09, 3.741140E+09, 3.996242E+09, 4.267501E+09,
4.555859E+09, 4.862310E+09, 5.187903E+09, 5.533738E+09, 5.900977E+09, 6.290843E+09,
6.704620E+09, 7.143662E+09, 7.609388E+09, 8.103298E+09, 8.626961E+09, 9.182033E+09,
9.770245E+09, 1.039343E+10, 1.105349E+10, 1.175245E+10, 1.249242E+10, 1.327562E+10,
1.410437E+10, 1.498112E+10, 1.590843E+10, 1.688899E+10, 1.792560E+10, 1.902123E+10,
2.017898E+10, 2.140208E+10, 2.269394E+10, 2.405813E+10, 2.549835E+10, 2.701854E+10,
2.862278E+10, 3.031533E+10, 3.210070E+10, 3.398356E+10, 3.596882E+10, 3.806161E+10,
4.026729E+10, 4.259149E+10,
])
# ---------------------- M = 40, I = 1 ---------------------------
M = 40
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
3.004878E+01, 1.395096E+03, 3.835885E+03, 7.029882E+03, 1.081742E+04, 1.511632E+04,
1.987927E+04, 2.508270E+04, 3.072275E+04, 3.681224E+04, 4.337811E+04, 5.045934E+04,
5.810536E+04, 6.637497E+04, 7.533555E+04, 8.506260E+04, 9.563945E+04, 1.071573E+05,
1.197152E+05, 1.334205E+05, 1.483892E+05, 1.647463E+05, 1.826266E+05, 2.021755E+05,
2.235493E+05, 2.469166E+05, 2.724590E+05, 3.003719E+05, 3.308659E+05, 3.641678E+05,
4.005214E+05, 4.401896E+05, 4.834550E+05, 5.306217E+05, 5.820165E+05, 6.379909E+05,
6.989225E+05, 7.652168E+05, 8.373091E+05, 9.156666E+05, 1.000790E+06, 1.093217E+06,
1.193523E+06, 1.302323E+06, 1.420279E+06, 1.548095E+06, 1.686527E+06, 1.836381E+06,
1.998520E+06, 2.173864E+06, 2.363397E+06, 2.568166E+06, 2.789290E+06, 3.027961E+06,
3.285448E+06, 3.563104E+06, 3.862368E+06, 4.184771E+06, 4.531944E+06, 4.905616E+06,
5.307629E+06, 5.739936E+06, 6.204613E+06, 6.703863E+06, 7.240020E+06, 7.815564E+06,
8.433119E+06, 9.095469E+06, 9.805559E+06, 1.056651E+07, 1.138163E+07, 1.225440E+07,
1.318853E+07, 1.418791E+07, 1.525668E+07, 1.639920E+07, 1.762006E+07, 1.892414E+07,
2.031655E+07, 2.180272E+07, 2.338834E+07, 2.507943E+07, 2.688234E+07, 2.880373E+07,
3.085064E+07, 3.303048E+07, 3.535103E+07, 3.782050E+07, 4.044751E+07, 4.324112E+07,
4.621087E+07, 4.936677E+07, 5.271934E+07, 5.627963E+07, 6.005924E+07, 6.407034E+07,
6.832572E+07, 7.283878E+07, 7.762356E+07, 8.269480E+07, 8.806795E+07, 9.375919E+07,
9.978547E+07, 1.061645E+08, 1.129150E+08, 1.200563E+08, 1.276088E+08, 1.355938E+08,
1.440336E+08, 1.529514E+08, 1.623717E+08, 1.723198E+08, 1.828225E+08, 1.939074E+08,
2.056036E+08, 2.179413E+08, 2.309523E+08, 2.446695E+08, 2.591273E+08, 2.743617E+08,
2.904102E+08, 3.073119E+08, 3.251074E+08, 3.438393E+08, 3.635518E+08, 3.842910E+08,
4.061049E+08, 4.290435E+08, 4.531588E+08, 4.785052E+08, 5.051389E+08, 5.331187E+08,
5.625056E+08, 5.933632E+08, 6.257574E+08, 6.597570E+08, 6.954334E+08, 7.328607E+08,
7.721161E+08, 8.132796E+08, 8.564345E+08, 9.016672E+08, 9.490674E+08, 9.987283E+08,
1.050747E+09, 1.105223E+09, 1.162261E+09, 1.221968E+09, 1.284458E+09, 1.349847E+09,
1.418253E+09, 1.489804E+09, 1.564628E+09, 1.642860E+09, 1.724638E+09, 1.810107E+09,
1.899416E+09, 1.992720E+09, 2.090179E+09, 2.191959E+09, 2.298231E+09, 2.409173E+09,
2.524970E+09, 2.645811E+09, 2.771894E+09, 2.903422E+09, 3.040606E+09, 3.183664E+09,
3.332821E+09, 3.488310E+09, 3.650373E+09, 3.819257E+09, 3.995222E+09, 4.178531E+09,
4.369461E+09, 4.568295E+09,
])
# ---------------------- M = 40, I = 2 ---------------------------
M = 40
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
3.014586E+01, 1.400380E+03, 3.850470E+03, 7.056645E+03, 1.085863E+04, 1.517397E+04,
1.995524E+04, 2.517891E+04, 3.084126E+04, 3.695530E+04, 4.354822E+04, 5.065922E+04,
5.833802E+04, 6.664374E+04, 7.564409E+04, 8.541493E+04, 9.604001E+04, 1.076110E+05,
1.202274E+05, 1.339971E+05, 1.490367E+05, 1.654718E+05, 1.834380E+05, 2.030813E+05,
2.245589E+05, 2.480404E+05, 2.737081E+05, 3.017585E+05, 3.324034E+05, 3.658707E+05,
4.024056E+05, 4.422723E+05, 4.857550E+05, 5.331592E+05, 5.848137E+05, 6.410718E+05,
7.023131E+05, 7.689453E+05, 8.414060E+05, 9.201650E+05, 1.005726E+06, 1.098629E+06,
1.199452E+06, 1.308815E+06, 1.427382E+06, 1.555862E+06, 1.695014E+06, 1.845650E+06,
2.008636E+06, 2.184898E+06, 2.375424E+06, 2.581269E+06, 2.803557E+06, 3.043485E+06,
3.302331E+06, 3.581455E+06, 3.882303E+06, 4.206416E+06, 4.555432E+06, 4.931091E+06,
5.335245E+06, 5.769857E+06, 6.237015E+06, 6.738933E+06, 7.277960E+06, 7.856588E+06,
8.477456E+06, 9.143363E+06, 9.857272E+06, 1.062232E+07, 1.144183E+07, 1.231931E+07,
1.325848E+07, 1.426327E+07, 1.533782E+07, 1.648652E+07, 1.771400E+07, 1.902515E+07,
2.042513E+07, 2.191937E+07, 2.351362E+07, 2.521392E+07, 2.702665E+07, 2.895852E+07,
3.101660E+07, 3.320835E+07, 3.554159E+07, 3.802456E+07, 4.066595E+07, 4.347487E+07,
4.646089E+07, 4.963410E+07, 5.300508E+07, 5.658492E+07, 6.038531E+07, 6.441848E+07,
6.869728E+07, 7.323518E+07, 7.804633E+07, 8.314553E+07, 8.854833E+07, 9.427099E+07,
1.003306E+08, 1.067449E+08, 1.135327E+08, 1.207135E+08, 1.283078E+08, 1.363370E+08,
1.448235E+08, 1.537908E+08, 1.632633E+08, 1.732667E+08, 1.838276E+08, 1.949741E+08,
2.067353E+08, 2.191416E+08, 2.322250E+08, 2.460185E+08, 2.605568E+08, 2.758761E+08,
2.920141E+08, 3.090099E+08, 3.269047E+08, 3.457411E+08, 3.655636E+08, 3.864186E+08,
4.083543E+08, 4.314211E+08, 4.556712E+08, 4.811593E+08, 5.079420E+08, 5.360784E+08,
5.656298E+08, 5.966602E+08, 6.292359E+08, 6.634261E+08, 6.993024E+08, 7.369397E+08,
7.764152E+08, 8.178097E+08, 8.612069E+08, 9.066936E+08, 9.543600E+08, 1.004300E+09,
1.056611E+09, 1.111393E+09, 1.168752E+09, 1.228795E+09, 1.291637E+09, 1.357393E+09,
1.426185E+09, 1.498139E+09, 1.573384E+09, 1.652057E+09, 1.734296E+09, 1.820247E+09,
1.910060E+09, 2.003890E+09, 2.101899E+09, 2.204253E+09, 2.311125E+09, 2.422694E+09,
2.539145E+09, 2.660669E+09, 2.787464E+09, 2.919735E+09, 3.057694E+09, 3.201561E+09,
3.351562E+09, 3.507931E+09, 3.670911E+09, 3.840752E+09, 4.017712E+09, 4.202060E+09,
4.394071E+09, 4.594031E+09,
])
# ---------------------- M = 41, I = 1 ---------------------------
M = 41
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
2.315131E+01, 1.079811E+03, 2.965691E+03, 5.436069E+03, 8.385490E+03, 1.180644E+04,
1.574958E+04, 2.029388E+04, 2.553119E+04, 3.156166E+04, 3.849468E+04, 4.645162E+04,
5.556906E+04, 6.600179E+04, 7.792578E+04, 9.154102E+04, 1.070745E+05, 1.247831E+05,
1.449571E+05, 1.679240E+05, 1.940520E+05, 2.237552E+05, 2.574980E+05, 2.958013E+05,
3.392479E+05, 3.884899E+05, 4.442559E+05, 5.073594E+05, 5.787080E+05, 6.593131E+05,
7.503014E+05, 8.529264E+05, 9.685822E+05, 1.098818E+06, 1.245353E+06, 1.410094E+06,
1.595158E+06, 1.802885E+06, 2.035868E+06, 2.296976E+06, 2.589376E+06, 2.916568E+06,
3.282415E+06, 3.691174E+06, 4.147540E+06, 4.656680E+06, 5.224279E+06, 5.856591E+06,
6.560485E+06, 7.343508E+06, 8.213936E+06, 9.180849E+06, 1.025420E+07, 1.144487E+07,
1.276479E+07, 1.422701E+07, 1.584577E+07, 1.763664E+07, 1.961662E+07, 2.180426E+07,
2.421975E+07, 2.688513E+07, 2.982439E+07, 3.306362E+07, 3.663123E+07, 4.055806E+07,
4.487764E+07, 4.962639E+07, 5.484381E+07, 6.057274E+07, 6.685963E+07, 7.375479E+07,
8.131271E+07, 8.959236E+07, 9.865750E+07, 1.085771E+08, 1.194257E+08, 1.312836E+08,
1.442379E+08, 1.583821E+08, 1.738174E+08, 1.906527E+08, 2.090052E+08, 2.290015E+08,
2.507774E+08, 2.744793E+08, 3.002646E+08, 3.283022E+08, 3.587741E+08, 3.918751E+08,
4.278148E+08, 4.668179E+08, 5.091253E+08, 5.549954E+08, 6.047049E+08, 6.585505E+08,
7.168495E+08, 7.799418E+08, 8.481907E+08, 9.219851E+08, 1.001740E+09, 1.087900E+09,
1.180939E+09, 1.281364E+09, 1.389714E+09, 1.506566E+09, 1.632536E+09, 1.768278E+09,
1.914492E+09, 2.071922E+09, 2.241360E+09, 2.423650E+09, 2.619690E+09, 2.830436E+09,
3.056904E+09, 3.300174E+09, 3.561394E+09, 3.841784E+09, 4.142639E+09, 4.465335E+09,
4.811331E+09, 5.182177E+09, 5.579515E+09, 6.005089E+09, 6.460744E+09, 6.948439E+09,
7.470246E+09, 8.028363E+09, 8.625114E+09, 9.262960E+09, 9.944506E+09, 1.067251E+10,
1.144988E+10, 1.227970E+10, 1.316522E+10, 1.410990E+10, 1.511735E+10, 1.619142E+10,
1.733617E+10, 1.855587E+10, 1.985503E+10, 2.123842E+10, 2.271106E+10, 2.427825E+10,
2.594556E+10, 2.771889E+10, 2.960444E+10, 3.160873E+10, 3.373864E+10, 3.600142E+10,
3.840468E+10, 4.095644E+10, 4.366516E+10, 4.653969E+10, 4.958938E+10, 5.282404E+10,
5.625398E+10, 5.989006E+10, 6.374364E+10, 6.782671E+10, 7.215183E+10, 7.673218E+10,
8.158161E+10, 8.671466E+10, 9.214658E+10, 9.789336E+10, 1.039718E+11, 1.103994E+11,
1.171948E+11, 1.243772E+11, 1.319668E+11, 1.399850E+11, 1.484539E+11, 1.573968E+11,
1.668382E+11, 1.768036E+11,
])
# ---------------------- M = 41, I = 2 ---------------------------
M = 41
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
4.756468E+01, 2.228120E+03, 6.122297E+03, 1.122300E+04, 1.731317E+04, 2.437845E+04,
3.252465E+04, 4.191614E+04, 5.274423E+04, 6.521817E+04, 7.956715E+04, 9.604612E+04,
1.149423E+05, 1.365814E+05, 1.613342E+05, 1.896217E+05, 2.219220E+05, 2.587765E+05,
3.007971E+05, 3.486737E+05, 4.031834E+05, 4.651993E+05, 5.357021E+05, 6.157912E+05,
7.066979E+05, 8.098005E+05, 9.266395E+05, 1.058936E+06, 1.208610E+06, 1.377803E+06,
1.568902E+06, 1.784563E+06, 2.027740E+06, 2.301720E+06, 2.610149E+06, 2.957077E+06,
3.346993E+06, 3.784872E+06, 4.276222E+06, 4.827137E+06, 5.444356E+06, 6.135322E+06,
6.908250E+06, 7.772205E+06, 8.737178E+06, 9.814173E+06, 1.101530E+07, 1.235389E+07,
1.384459E+07, 1.550347E+07, 1.734820E+07, 1.939814E+07, 2.167453E+07, 2.420060E+07,
2.700184E+07, 3.010607E+07, 3.354375E+07, 3.734815E+07, 4.155558E+07, 4.620567E+07,
5.134166E+07, 5.701064E+07, 6.326393E+07, 7.015737E+07, 7.775173E+07, 8.611306E+07,
9.531315E+07, 1.054300E+08, 1.165481E+08, 1.287594E+08, 1.421633E+08, 1.568678E+08,
1.729895E+08, 1.906549E+08, 2.100008E+08, 2.311751E+08, 2.543376E+08, 2.796608E+08,
3.073314E+08, 3.375503E+08, 3.705345E+08, 4.065179E+08, 4.457524E+08, 4.885096E+08,
5.350814E+08, 5.857824E+08, 6.409505E+08, 7.009493E+08, 7.661692E+08, 8.370298E+08,
9.139812E+08, 9.975068E+08, 1.088125E+09, 1.186391E+09, 1.292901E+09, 1.408292E+09,
1.533248E+09, 1.668500E+09, 1.814831E+09, 1.973077E+09, 2.144133E+09, 2.328955E+09,
2.528564E+09, 2.744052E+09, 2.976580E+09, 3.227394E+09, 3.497816E+09, 3.789260E+09,
4.103232E+09, 4.441336E+09, 4.805281E+09, 5.196886E+09, 5.618088E+09, 6.070948E+09,
6.557656E+09, 7.080543E+09, 7.642086E+09, 8.244916E+09, 8.891828E+09, 9.585791E+09,
1.032996E+10, 1.112766E+10, 1.198246E+10, 1.289812E+10, 1.387861E+10, 1.492818E+10,
1.605128E+10, 1.725268E+10, 1.853739E+10, 1.991072E+10, 2.137831E+10, 2.294611E+10,
2.462041E+10, 2.640788E+10, 2.831554E+10, 3.035083E+10, 3.252162E+10, 3.483620E+10,
3.730333E+10, 3.993226E+10, 4.273276E+10, 4.571512E+10, 4.889021E+10, 5.226948E+10,
5.586501E+10, 5.968954E+10, 6.375646E+10, 6.807992E+10, 7.267480E+10, 7.755676E+10,
8.274231E+10, 8.824879E+10, 9.409449E+10, 1.002986E+11, 1.068814E+11, 1.138640E+11,
1.212689E+11, 1.291194E+11, 1.374404E+11, 1.462576E+11, 1.555982E+11, 1.654910E+11,
1.759658E+11, 1.870541E+11, 1.987891E+11, 2.112053E+11, 2.243391E+11, 2.382286E+11,
2.529139E+11, 2.684368E+11, 2.848412E+11, 3.021733E+11, 3.204811E+11, 3.398152E+11,
3.602284E+11, 3.817762E+11,
])
# ---------------------- M = 41, I = 3 ---------------------------
M = 41
I = 3
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
4.640829E+01, 2.165303E+03, 5.949088E+03, 1.090578E+04, 1.682930E+04, 2.371551E+04,
3.167737E+04, 4.088260E+04, 5.152311E+04, 6.380713E+04, 7.796181E+04, 9.423931E+04,
1.129235E+05, 1.343366E+05, 1.588447E+05, 1.868645E+05, 2.188692E+05, 2.553945E+05,
2.970463E+05, 3.445084E+05, 3.985504E+05, 4.600380E+05, 5.299431E+05, 6.093559E+05,
6.994976E+05, 8.017346E+05, 9.175952E+05, 1.048786E+06, 1.197212E+06, 1.364999E+06,
1.554512E+06, 1.768388E+06, 2.009559E+06, 2.281283E+06, 2.587180E+06, 2.931268E+06,
3.318001E+06, 3.752316E+06, 4.239679E+06, 4.786137E+06, 5.398376E+06, 6.083784E+06,
6.850514E+06, 7.707561E+06, 8.664841E+06, 9.733275E+06, 1.092489E+07, 1.225290E+07,
1.373184E+07, 1.537769E+07, 1.720796E+07, 1.924188E+07, 2.150051E+07, 2.400695E+07,
2.678645E+07, 2.986667E+07, 3.327782E+07, 3.705292E+07, 4.122803E+07, 4.584250E+07,
5.093921E+07, 5.656495E+07, 6.277064E+07, 6.961172E+07, 7.714850E+07, 8.544659E+07,
9.457722E+07, 1.046178E+08, 1.156524E+08, 1.277720E+08, 1.410755E+08, 1.556700E+08,
1.716714E+08, 1.892051E+08, 2.084071E+08, 2.294241E+08, 2.524148E+08, 2.775507E+08,
3.050167E+08, 3.350126E+08, 3.677537E+08, 4.034723E+08, 4.424186E+08, 4.848620E+08,
5.310927E+08, 5.814227E+08, 6.361877E+08, 6.957486E+08, 7.604932E+08, 8.308379E+08,
9.072299E+08, 9.901490E+08, 1.080110E+09, 1.177664E+09, 1.283403E+09, 1.397960E+09,
1.522014E+09, 1.656291E+09, 1.801568E+09, 1.958675E+09, 2.128501E+09, 2.311996E+09,
2.510172E+09, 2.724114E+09, 2.954978E+09, 3.203997E+09, 3.472486E+09, 3.761848E+09,
4.073580E+09, 4.409273E+09, 4.770625E+09, 5.159443E+09, 5.577650E+09, 6.027291E+09,
6.510543E+09, 7.029720E+09, 7.587282E+09, 8.185841E+09, 8.828174E+09, 9.517228E+09,
1.025613E+10, 1.104820E+10, 1.189697E+10, 1.280617E+10, 1.377975E+10, 1.482192E+10,
1.593712E+10, 1.713006E+10, 1.840573E+10, 1.976942E+10, 2.122670E+10, 2.278350E+10,
2.444605E+10, 2.622098E+10, 2.811528E+10, 3.013632E+10, 3.229192E+10, 3.459030E+10,
3.704019E+10, 3.965075E+10, 4.243169E+10, 4.539323E+10, 4.854616E+10, 5.190187E+10,
5.547234E+10, 5.927022E+10, 6.330882E+10, 6.760219E+10, 7.216510E+10, 7.701311E+10,
8.216261E+10, 8.763084E+10, 9.343594E+10, 9.959700E+10, 1.061341E+11, 1.130683E+11,
1.204218E+11, 1.282180E+11, 1.364812E+11, 1.452374E+11, 1.545134E+11, 1.643377E+11,
1.747400E+11, 1.857517E+11, 1.974056E+11, 2.097360E+11, 2.227790E+11, 2.365727E+11,
2.511566E+11, 2.665724E+11, 2.828637E+11, 3.000762E+11, 3.182578E+11, 3.374587E+11,
3.577313E+11, 3.791307E+11,
])
# ---------------------- M = 41, I = 4 ---------------------------
M = 41
I = 4
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
9.539916E+01, 4.470892E+03, 1.228501E+04, 2.252154E+04, 3.475468E+04, 4.897554E+04,
6.541725E+04, 8.442665E+04, 1.064017E+05, 1.317753E+05, 1.610212E+05, 1.946663E+05,
2.333045E+05, 2.776101E+05, 3.283502E+05, 3.863976E+05, 4.527432E+05, 5.285101E+05,
6.149681E+05, 7.135503E+05, 8.258710E+05, 9.537460E+05, 1.099215E+06, 1.264566E+06,
1.452364E+06, 1.665478E+06, 1.907122E+06, 2.180882E+06, 2.490766E+06, 2.841242E+06,
3.237293E+06, 3.684466E+06, 4.188938E+06, 4.757571E+06, 5.397996E+06, 6.118680E+06,
6.929018E+06, 7.839421E+06, 8.861424E+06, 1.000779E+07, 1.129264E+07, 1.273156E+07,
1.434179E+07, 1.614232E+07, 1.815412E+07, 2.040028E+07, 2.290621E+07, 2.569988E+07,
2.881206E+07, 3.227651E+07, 3.613034E+07, 4.041424E+07, 4.517284E+07, 5.045502E+07,
5.631431E+07, 6.280930E+07, 7.000402E+07, 7.796850E+07, 8.677916E+07, 9.651945E+07,
1.072804E+08, 1.191612E+08, 1.322698E+08, 1.467241E+08, 1.626520E+08, 1.801928E+08,
1.994978E+08, 2.207313E+08, 2.440718E+08, 2.697130E+08, 2.978648E+08, 3.287546E+08,
3.626291E+08, 3.997549E+08, 4.404208E+08, 4.849391E+08, 5.336473E+08, 5.869099E+08,
6.451208E+08, 7.087047E+08, 7.781201E+08, 8.538612E+08, 9.364606E+08, 1.026492E+09,
1.124573E+09, 1.231368E+09, 1.347593E+09, 1.474015E+09, 1.611462E+09, 1.760820E+09,
1.923041E+09, 2.099150E+09, 2.290241E+09, 2.497492E+09, 2.722164E+09, 2.965606E+09,
3.229265E+09, 3.514690E+09, 3.823537E+09, 4.157580E+09, 4.518712E+09, 4.908959E+09,
5.330485E+09, 5.785601E+09, 6.276774E+09, 6.806635E+09, 7.377994E+09, 7.993846E+09,
8.657381E+09, 9.372002E+09, 1.014133E+10, 1.096923E+10, 1.185980E+10, 1.281742E+10,
1.384672E+10, 1.495267E+10, 1.614050E+10, 1.741580E+10, 1.878451E+10, 2.025292E+10,
2.182772E+10, 2.351600E+10, 2.532529E+10, 2.726358E+10, 2.933934E+10, 3.156152E+10,
3.393966E+10, 3.648380E+10, 3.920463E+10, 4.211343E+10, 4.522214E+10, 4.854343E+10,
5.209065E+10, 5.587795E+10, 5.992028E+10, 6.423345E+10, 6.883414E+10, 7.373998E+10,
7.896960E+10, 8.454265E+10, 9.047989E+10, 9.680319E+10, 1.035357E+11, 1.107016E+11,
1.183268E+11, 1.264382E+11, 1.350644E+11, 1.442354E+11, 1.539828E+11, 1.643401E+11,
1.753422E+11, 1.870260E+11, 1.994305E+11, 2.125965E+11, 2.265670E+11, 2.413872E+11,
2.571045E+11, 2.737690E+11, 2.914332E+11, 3.101521E+11, 3.299836E+11, 3.509886E+11,
3.732309E+11, 3.967775E+11, 4.216988E+11, 4.480684E+11, 4.759638E+11, 5.054661E+11,
5.366605E+11, 5.696362E+11, 6.044865E+11, 6.413096E+11, 6.802081E+11, 7.212894E+11,
7.646663E+11, 8.104566E+11,
])
# ---------------------- M = 42, I = 1 ---------------------------
M = 42
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00,
0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00,
0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00,
0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00,
0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00,
0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00,
0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00,
0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00,
0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00,
0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00,
0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00,
0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00,
0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00,
0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00,
0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00,
0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00,
0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00,
0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00,
0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00,
0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00,
0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00,
0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00,
0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00,
0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00,
0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00,
0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00,
0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00,
0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00,
0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00, 0.000000E+00,
0.000000E+00, 0.000000E+00,
])
# ---------------------- M = 43, I = 1 ---------------------------
M = 43
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.019112E+01, 1.905753E+02, 3.807665E+02, 5.762817E+02, 7.905060E+02, 1.038159E+03,
1.333345E+03, 1.691617E+03, 2.131702E+03, 2.676627E+03, 3.354709E+03, 4.200671E+03,
5.256974E+03, 6.575423E+03, 8.219051E+03, 1.026434E+04, 1.280384E+04, 1.594915E+04,
1.983447E+04, 2.462068E+04, 3.050003E+04, 3.770160E+04, 4.649756E+04, 5.721039E+04,
7.022113E+04, 8.597887E+04, 1.050115E+05, 1.279381E+05, 1.554830E+05, 1.884915E+05,
2.279483E+05, 2.749977E+05, 3.309668E+05, 3.973918E+05, 4.760472E+05, 5.689788E+05,
6.785415E+05, 8.074402E+05, 9.587774E+05, 1.136105E+06, 1.343483E+06, 1.585544E+06,
1.867567E+06, 2.195558E+06, 2.576338E+06, 3.017647E+06, 3.528251E+06, 4.118066E+06,
4.798292E+06, 5.581567E+06, 6.482126E+06, 7.515994E+06, 8.701176E+06, 1.005789E+07,
1.160879E+07, 1.337926E+07, 1.539769E+07, 1.769582E+07, 2.030904E+07, 2.327685E+07,
2.664322E+07, 3.045709E+07, 3.477285E+07, 3.965090E+07, 4.515827E+07, 5.136922E+07,
5.836599E+07, 6.623958E+07, 7.509054E+07, 8.502994E+07, 9.618032E+07, 1.086768E+08,
1.226681E+08, 1.383180E+08, 1.558067E+08, 1.753321E+08, 1.971114E+08, 2.213829E+08,
2.484083E+08, 2.784735E+08, 3.118921E+08, 3.490069E+08, 3.901925E+08, 4.358585E+08,
4.864516E+08, 5.424591E+08, 6.044125E+08, 6.728905E+08, 7.485237E+08, 8.319970E+08,
9.240564E+08, 1.025512E+09, 1.137243E+09, 1.260205E+09, 1.395433E+09, 1.544051E+09,
1.707273E+09, 1.886418E+09, 2.082910E+09, 2.298291E+09, 2.534229E+09, 2.792522E+09,
3.075116E+09, 3.384109E+09, 3.721764E+09, 4.090522E+09, 4.493014E+09, 4.932072E+09,
5.410747E+09, 5.932319E+09, 6.500319E+09, 7.118541E+09, 7.791064E+09, 8.522268E+09,
9.316850E+09, 1.017986E+10, 1.111670E+10, 1.213319E+10, 1.323554E+10, 1.443039E+10,
1.572490E+10, 1.712669E+10, 1.864393E+10, 2.028536E+10, 2.206030E+10, 2.397874E+10,
2.605133E+10, 2.828946E+10, 3.070527E+10, 3.331171E+10, 3.612261E+10, 3.915271E+10,
4.241771E+10, 4.593434E+10, 4.972043E+10, 5.379494E+10, 5.817805E+10, 6.289125E+10,
6.795741E+10, 7.340077E+10, 7.924717E+10, 8.552402E+10, 9.226047E+10, 9.948742E+10,
1.072377E+11, 1.155462E+11, 1.244497E+11, 1.339876E+11, 1.442013E+11, 1.551348E+11,
1.668348E+11, 1.793507E+11, 1.927348E+11, 2.070423E+11, 2.223319E+11, 2.386657E+11,
2.561090E+11, 2.747312E+11, 2.946056E+11, 3.158095E+11, 3.384248E+11, 3.625376E+11,
3.882395E+11, 4.156265E+11, 4.448002E+11, 4.758678E+11, 5.089422E+11, 5.441429E+11,
5.815953E+11, 6.214321E+11, 6.637927E+11, 7.088241E+11, 7.566812E+11, 8.075271E+11,
8.615336E+11, 9.188811E+11,
])
# ---------------------- M = 44, I = 1 ---------------------------
M = 44
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
2.957662E+01, 5.518291E+02, 1.102633E+03, 1.668279E+03, 2.286281E+03, 2.997450E+03,
3.838554E+03, 4.846743E+03, 6.063697E+03, 7.537981E+03, 9.326765E+03, 1.149723E+04,
1.412808E+04, 1.731129E+04, 2.115428E+04, 2.578174E+04, 3.133815E+04, 3.799064E+04,
4.593183E+04, 5.538330E+04, 6.659913E+04, 7.986998E+04, 9.552786E+04, 1.139507E+05,
1.355685E+05, 1.608685E+05, 1.904027E+05, 2.247946E+05, 2.647478E+05, 3.110535E+05,
3.646019E+05, 4.263913E+05, 4.975399E+05, 5.792987E+05, 6.730643E+05, 7.803938E+05,
9.030217E+05, 1.042876E+06, 1.202097E+06, 1.383060E+06, 1.588392E+06, 1.821003E+06,
2.084105E+06, 2.381241E+06, 2.716320E+06, 3.093641E+06, 3.517933E+06, 3.994391E+06,
4.528714E+06, 5.127151E+06, 5.796541E+06, 6.544370E+06, 7.378816E+06, 8.308808E+06,
9.344088E+06, 1.049527E+07, 1.177392E+07, 1.319260E+07, 1.476499E+07, 1.650593E+07,
1.843154E+07, 2.055927E+07, 2.290806E+07, 2.549840E+07, 2.835243E+07, 3.149414E+07,
3.494939E+07, 3.874612E+07, 4.291446E+07, 4.748690E+07, 5.249840E+07, 5.798665E+07,
6.399214E+07, 7.055842E+07, 7.773230E+07, 8.556400E+07, 9.410744E+07, 1.034204E+08,
1.135649E+08, 1.246073E+08, 1.366186E+08, 1.496748E+08, 1.638572E+08, 1.792528E+08,
1.959542E+08, 2.140605E+08, 2.336775E+08, 2.549180E+08, 2.779020E+08, 3.027576E+08,
3.296212E+08, 3.586378E+08, 3.899621E+08, 4.237580E+08, 4.602001E+08, 4.994740E+08,
5.417764E+08, 5.873165E+08, 6.363159E+08, 6.890100E+08, 7.456480E+08, 8.064942E+08,
8.718285E+08, 9.419473E+08, 1.017164E+09, 1.097811E+09, 1.184239E+09, 1.276819E+09,
1.375943E+09, 1.482026E+09, 1.595504E+09, 1.716839E+09, 1.846519E+09, 1.985058E+09,
2.132997E+09, 2.290908E+09, 2.459392E+09, 2.639085E+09, 2.830653E+09, 3.034800E+09,
3.252265E+09, 3.483827E+09, 3.730304E+09, 3.992557E+09, 4.271490E+09, 4.568055E+09,
4.883248E+09, 5.218118E+09, 5.573767E+09, 5.951348E+09, 6.352075E+09, 6.777217E+09,
7.228108E+09, 7.706145E+09, 8.212792E+09, 8.749583E+09, 9.318127E+09, 9.920104E+09,
1.055728E+10, 1.123149E+10, 1.194468E+10, 1.269885E+10, 1.349612E+10, 1.433869E+10,
1.522888E+10, 1.616909E+10, 1.716185E+10, 1.820978E+10, 1.931564E+10, 2.048228E+10,
2.171272E+10, 2.301006E+10, 2.437758E+10, 2.581868E+10, 2.733690E+10, 2.893594E+10,
3.061966E+10, 3.239208E+10, 3.425739E+10, 3.621994E+10, 3.828429E+10, 4.045517E+10,
4.273749E+10, 4.513640E+10, 4.765723E+10, 5.030553E+10, 5.308708E+10, 5.600790E+10,
5.907422E+10, 6.229255E+10, 6.566964E+10, 6.921252E+10, 7.292848E+10, 7.682509E+10,
8.091023E+10, 8.519208E+10,
])
# ---------------------- M = 44, I = 2 ---------------------------
M = 44
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
2.026479E+01, 3.788677E+02, 7.570816E+02, 1.145507E+03, 1.569915E+03, 2.058345E+03,
2.636068E+03, 3.328628E+03, 4.164690E+03, 5.177665E+03, 6.406892E+03, 7.898611E+03,
9.707022E+03, 1.189546E+04, 1.453793E+04, 1.772040E+04, 2.154245E+04, 2.611938E+04,
3.158409E+04, 3.808955E+04, 4.581120E+04, 5.494989E+04, 6.573509E+04, 7.842821E+04,
9.332667E+04, 1.107679E+05, 1.311340E+05, 1.548574E+05, 1.824256E+05, 2.143877E+05,
2.513616E+05, 2.940409E+05, 3.432024E+05, 3.997164E+05, 4.645548E+05, 5.388019E+05,
6.236663E+05, 7.204925E+05, 8.307742E+05, 9.561694E+05, 1.098515E+06, 1.259846E+06,
1.442409E+06, 1.648688E+06, 1.881421E+06, 2.143626E+06, 2.438622E+06, 2.770061E+06,
3.141951E+06, 3.558689E+06, 4.025097E+06, 4.546452E+06, 5.128527E+06, 5.777632E+06,
6.500654E+06, 7.305109E+06, 8.199187E+06, 9.191807E+06, 1.029267E+07, 1.151234E+07,
1.286226E+07, 1.435488E+07, 1.600369E+07, 1.782331E+07, 1.982959E+07, 2.203965E+07,
2.447203E+07, 2.714675E+07, 3.008544E+07, 3.331144E+07, 3.684992E+07, 4.072800E+07,
4.497489E+07, 4.962204E+07, 5.470328E+07, 6.025497E+07, 6.631617E+07, 7.292885E+07,
8.013801E+07, 8.799194E+07, 9.654241E+07, 1.058449E+08, 1.159587E+08, 1.269474E+08,
1.388789E+08, 1.518261E+08, 1.658664E+08, 1.810828E+08, 1.975638E+08, 2.154040E+08,
2.347039E+08, 2.555711E+08, 2.781199E+08, 3.024721E+08, 3.287576E+08, 3.571142E+08,
3.876889E+08, 4.206376E+08, 4.561262E+08, 4.943310E+08, 5.354389E+08, 5.796485E+08,
6.271706E+08, 6.782284E+08, 7.330590E+08, 7.919132E+08, 8.550571E+08, 9.227721E+08,
9.953563E+08, 1.073125E+09, 1.156413E+09, 1.245571E+09, 1.340974E+09, 1.443015E+09,
1.552110E+09, 1.668700E+09, 1.793248E+09, 1.926244E+09, 2.068205E+09, 2.219676E+09,
2.381232E+09, 2.553477E+09, 2.737051E+09, 2.932625E+09, 3.140908E+09, 3.362643E+09,
3.598617E+09, 3.849653E+09, 4.116620E+09, 4.400430E+09, 4.702043E+09, 5.022468E+09,
5.362763E+09, 5.724042E+09, 6.107473E+09, 6.514284E+09, 6.945762E+09, 7.403258E+09,
7.888191E+09, 8.402048E+09, 8.946386E+09, 9.522842E+09, 1.013313E+10, 1.077904E+10,
1.146245E+10, 1.218534E+10, 1.294977E+10, 1.375789E+10, 1.461196E+10, 1.551435E+10,
1.646753E+10, 1.747409E+10, 1.853673E+10, 1.965827E+10, 2.084169E+10, 2.209005E+10,
2.340660E+10, 2.479470E+10, 2.625787E+10, 2.779978E+10, 2.942429E+10, 3.113539E+10,
3.293726E+10, 3.483426E+10, 3.683096E+10, 3.893208E+10, 4.114258E+10, 4.346763E+10,
4.591259E+10, 4.848308E+10, 5.118493E+10, 5.402423E+10, 5.700733E+10, 6.014081E+10,
6.343157E+10, 6.688675E+10,
])
# ---------------------- M = 44, I = 3 ---------------------------
M = 44
I = 3
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
5.938131E+01, 1.108247E+03, 2.214517E+03, 3.350727E+03, 4.592300E+03, 6.021313E+03,
7.711766E+03, 9.738490E+03, 1.218549E+04, 1.515071E+04, 1.874951E+04, 2.311752E+04,
2.841381E+04, 3.482427E+04, 4.256628E+04, 5.189234E+04, 6.309521E+04, 7.651387E+04,
9.253932E+04, 1.116218E+05, 1.342779E+05, 1.610995E+05, 1.927630E+05, 2.300395E+05,
2.738067E+05, 3.250609E+05, 3.849317E+05, 4.546970E+05, 5.357995E+05, 6.298651E+05,
7.387238E+05, 8.644319E+05, 1.009295E+06, 1.175895E+06, 1.367122E+06, 1.586198E+06,
1.836720E+06, 2.122693E+06, 2.448568E+06, 2.819290E+06, 3.240346E+06, 3.717812E+06,
4.258411E+06, 4.869576E+06, 5.559511E+06, 6.337263E+06, 7.212800E+06, 8.197089E+06,
9.302184E+06, 1.054133E+07, 1.192904E+07, 1.348124E+07, 1.521536E+07, 1.715048E+07,
1.930742E+07, 2.170896E+07, 2.437993E+07, 2.734741E+07, 3.064087E+07, 3.429243E+07,
3.833699E+07, 4.281248E+07, 4.776010E+07, 5.322453E+07, 5.925423E+07, 6.590171E+07,
7.322381E+07, 8.128204E+07, 9.014290E+07, 9.987824E+07, 1.105657E+08, 1.222889E+08,
1.351382E+08, 1.492109E+08, 1.646119E+08, 1.814539E+08, 1.998583E+08, 2.199558E+08,
2.418865E+08, 2.658011E+08, 2.918612E+08, 3.202405E+08, 3.511248E+08, 3.847136E+08,
4.212204E+08, 4.608738E+08, 5.039185E+08, 5.506162E+08, 6.012466E+08, 6.561088E+08,
7.155218E+08, 7.798268E+08, 8.493872E+08, 9.245912E+08, 1.005852E+09, 1.093611E+09,
1.188338E+09, 1.290532E+09, 1.400726E+09, 1.519485E+09, 1.647413E+09, 1.785148E+09,
1.933372E+09, 2.092806E+09, 2.264219E+09, 2.448424E+09, 2.646286E+09, 2.858721E+09,
3.086701E+09, 3.331257E+09, 3.593479E+09, 3.874525E+09, 4.175617E+09, 4.498052E+09,
4.843200E+09, 5.212513E+09, 5.607523E+09, 6.029851E+09, 6.481212E+09, 6.963415E+09,
7.478371E+09, 8.028099E+09, 8.614729E+09, 9.240508E+09, 9.907808E+09, 1.061913E+10,
1.137711E+10, 1.218453E+10, 1.304431E+10, 1.395953E+10, 1.493346E+10, 1.596950E+10,
1.707126E+10, 1.824252E+10, 1.948726E+10, 2.080968E+10, 2.221419E+10, 2.370541E+10,
2.528822E+10, 2.696772E+10, 2.874930E+10, 3.063859E+10, 3.264151E+10, 3.476429E+10,
3.701345E+10, 3.939584E+10, 4.191864E+10, 4.458939E+10, 4.741597E+10, 5.040668E+10,
5.357018E+10, 5.691557E+10, 6.045237E+10, 6.419056E+10, 6.814058E+10, 7.231337E+10,
7.672038E+10, 8.137359E+10, 8.628553E+10, 9.146932E+10, 9.693866E+10, 1.027079E+11,
1.087920E+11, 1.152067E+11, 1.219684E+11, 1.290941E+11, 1.366017E+11, 1.445099E+11,
1.528383E+11, 1.616071E+11, 1.708377E+11, 1.805521E+11, 1.907737E+11, 2.015265E+11,
2.128357E+11, 2.247276E+11,
])
# ---------------------- M = 44, I = 4 ---------------------------
M = 44
I = 4
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
5.938661E+01, 1.108336E+03, 2.214589E+03, 3.350570E+03, 4.591574E+03, 6.019498E+03,
7.708091E+03, 9.731833E+03, 1.217425E+04, 1.513263E+04, 1.872146E+04, 2.307521E+04,
2.835138E+04, 3.473390E+04, 4.243751E+04, 5.171140E+04, 6.284408E+04, 7.616915E+04,
9.207088E+04, 1.109911E+05, 1.334360E+05, 1.599844E+05, 1.912971E+05, 2.281258E+05,
2.713248E+05, 3.218620E+05, 3.808325E+05, 4.494733E+05, 5.291779E+05, 6.215133E+05,
7.282402E+05, 8.513320E+05, 9.929969E+05, 1.155703E+06, 1.342204E+06, 1.555566E+06,
1.799202E+06, 2.076901E+06, 2.392867E+06, 2.751758E+06, 3.158724E+06, 3.619459E+06,
4.140242E+06, 4.727999E+06, 5.390348E+06, 6.135671E+06, 6.973171E+06, 7.912946E+06,
8.966061E+06, 1.014463E+07, 1.146190E+07, 1.293235E+07, 1.457177E+07, 1.639738E+07,
1.842795E+07, 2.068389E+07, 2.318741E+07, 2.596262E+07, 2.903569E+07, 3.243502E+07,
3.619134E+07, 4.033796E+07, 4.491089E+07, 4.994905E+07, 5.549449E+07, 6.159258E+07,
6.829223E+07, 7.564616E+07, 8.371114E+07, 9.254824E+07, 1.022231E+08, 1.128064E+08,
1.243737E+08, 1.370064E+08, 1.507918E+08, 1.658230E+08, 1.822004E+08, 2.000307E+08,
2.194287E+08, 2.405167E+08, 2.634254E+08, 2.882945E+08, 3.152730E+08, 3.445196E+08,
3.762037E+08, 4.105057E+08, 4.476175E+08, 4.877435E+08, 5.311009E+08, 5.779207E+08,
6.284482E+08, 6.829439E+08, 7.416843E+08, 8.049627E+08, 8.730900E+08, 9.463957E+08,
1.025229E+09, 1.109959E+09, 1.200977E+09, 1.298696E+09, 1.403554E+09, 1.516013E+09,
1.636560E+09, 1.765712E+09, 1.904012E+09, 2.052033E+09, 2.210381E+09, 2.379692E+09,
2.560640E+09, 2.753931E+09, 2.960311E+09, 3.180562E+09, 3.415509E+09, 3.666020E+09,
3.933004E+09, 4.217420E+09, 4.520273E+09, 4.842618E+09, 5.185562E+09, 5.550269E+09,
5.937956E+09, 6.349900E+09, 6.787441E+09, 7.251982E+09, 7.744989E+09, 8.268002E+09,
8.822629E+09, 9.410552E+09, 1.003353E+10, 1.069341E+10, 1.139211E+10, 1.213163E+10,
1.291408E+10, 1.374165E+10, 1.461662E+10, 1.554138E+10, 1.651842E+10, 1.755032E+10,
1.863980E+10, 1.978967E+10, 2.100286E+10, 2.228244E+10, 2.363158E+10, 2.505360E+10,
2.655195E+10, 2.813020E+10, 2.979208E+10, 3.154147E+10, 3.338240E+10, 3.531905E+10,
3.735576E+10, 3.949704E+10, 4.174758E+10, 4.411223E+10, 4.659605E+10, 4.920424E+10,
5.194225E+10, 5.481567E+10, 5.783035E+10, 6.099230E+10, 6.430779E+10, 6.778327E+10,
7.142544E+10, 7.524123E+10, 7.923781E+10, 8.342260E+10, 8.780325E+10, 9.238769E+10,
9.718412E+10, 1.022010E+11, 1.074470E+11, 1.129313E+11, 1.186630E+11, 1.246519E+11,
1.309079E+11, 1.374411E+11,
])
# ---------------------- M = 44, I = 5 ---------------------------
M = 44
I = 5
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
6.090077E+01, 1.138773E+03, 2.275792E+03, 3.443921E+03, 4.720878E+03, 6.191305E+03,
7.931657E+03, 1.001940E+04, 1.254164E+04, 1.560008E+04, 1.931466E+04, 2.382667E+04,
2.930200E+04, 3.593494E+04, 4.395300E+04, 5.362102E+04, 6.524665E+04, 7.918681E+04,
9.585408E+04, 1.157246E+05, 1.393459E+05, 1.673468E+05, 2.004476E+05, 2.394715E+05,
2.853580E+05, 3.391763E+05, 4.021417E+05, 4.756331E+05, 5.612115E+05, 6.606413E+05,
7.759143E+05, 9.092750E+05, 1.063248E+06, 1.240670E+06, 1.444723E+06, 1.678972E+06,
1.947406E+06, 2.254480E+06, 2.605165E+06, 3.005004E+06, 3.460163E+06, 3.977501E+06,
4.564632E+06, 5.230003E+06, 5.982976E+06, 6.833909E+06, 7.794262E+06, 8.876687E+06,
1.009515E+07, 1.146505E+07, 1.300334E+07, 1.472869E+07, 1.666160E+07, 1.882462E+07,
2.124247E+07, 2.394228E+07, 2.695376E+07, 3.030945E+07, 3.404492E+07, 3.819908E+07,
4.281440E+07, 4.793725E+07, 5.361820E+07, 5.991235E+07, 6.687972E+07, 7.458561E+07,
8.310104E+07, 9.250322E+07, 1.028760E+08, 1.143103E+08, 1.269049E+08, 1.407668E+08,
1.560118E+08, 1.727656E+08, 1.911638E+08, 2.113534E+08, 2.334931E+08, 2.577542E+08,
2.843221E+08, 3.133963E+08, 3.451922E+08, 3.799422E+08, 4.178961E+08, 4.593235E+08,
5.045140E+08, 5.537796E+08, 6.074553E+08, 6.659014E+08, 7.295048E+08, 7.986808E+08,
8.738752E+08, 9.555661E+08, 1.044266E+09, 1.140524E+09, 1.244930E+09, 1.358112E+09,
1.480745E+09, 1.613551E+09, 1.757301E+09, 1.912819E+09, 2.080987E+09, 2.262745E+09,
2.459099E+09, 2.671121E+09, 2.899954E+09, 3.146818E+09, 3.413014E+09, 3.699928E+09,
4.009035E+09, 4.341907E+09, 4.700218E+09, 5.085748E+09, 5.500390E+09, 5.946158E+09,
6.425194E+09, 6.939770E+09, 7.492304E+09, 8.085362E+09, 8.721666E+09, 9.404108E+09,
1.013575E+10, 1.091986E+10, 1.175986E+10, 1.265943E+10, 1.362243E+10, 1.465296E+10,
1.575536E+10, 1.693424E+10, 1.819446E+10, 1.954117E+10, 2.097981E+10, 2.251614E+10,
2.415625E+10, 2.590657E+10, 2.777389E+10, 2.976540E+10, 3.188867E+10, 3.415170E+10,
3.656294E+10, 3.913129E+10, 4.186616E+10, 4.477745E+10, 4.787562E+10, 5.117167E+10,
5.467720E+10, 5.840445E+10, 6.236629E+10, 6.657628E+10, 7.104869E+10, 7.579854E+10,
8.084163E+10, 8.619460E+10, 9.187493E+10, 9.790101E+10, 1.042922E+11, 1.110687E+11,
1.182520E+11, 1.258645E+11, 1.339296E+11, 1.424723E+11, 1.515183E+11, 1.610951E+11,
1.712311E+11, 1.819564E+11, 1.933025E+11, 2.053023E+11, 2.179905E+11, 2.314034E+11,
2.455790E+11, 2.605572E+11, 2.763796E+11, 2.930901E+11, 3.107345E+11, 3.293607E+11,
3.490188E+11, 3.697615E+11,
])
# ---------------------- M = 44, I = 6 ---------------------------
M = 44
I = 6
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
4.755283E+01, 8.917192E+02, 1.782495E+03, 2.702949E+03, 3.721375E+03, 4.909994E+03,
6.337485E+03, 8.078305E+03, 1.021922E+04, 1.286259E+04, 1.613004E+04, 2.016576E+04,
2.514031E+04, 3.125496E+04, 3.874699E+04, 4.789457E+04, 5.902344E+04, 7.251376E+04,
8.880723E+04, 1.084165E+05, 1.319339E+05, 1.600430E+05, 1.935301E+05, 2.332972E+05,
2.803772E+05, 3.359502E+05, 4.013610E+05, 4.781392E+05, 5.680217E+05, 6.729762E+05,
7.952284E+05, 9.372903E+05, 1.101993E+06, 1.292522E+06, 1.512454E+06, 1.765801E+06,
2.057055E+06, 2.391235E+06, 2.773948E+06, 3.211440E+06, 3.710667E+06, 4.279359E+06,
4.926097E+06, 5.660397E+06, 6.492793E+06, 7.434939E+06, 8.499703E+06, 9.701287E+06,
1.105534E+07, 1.257909E+07, 1.429147E+07, 1.621329E+07, 1.836739E+07, 2.077878E+07,
2.347487E+07, 2.648563E+07, 2.984384E+07, 3.358525E+07, 3.774889E+07, 4.237729E+07,
4.751677E+07, 5.321771E+07, 5.953488E+07, 6.652777E+07, 7.426095E+07, 8.280442E+07,
9.223405E+07, 1.026320E+08, 1.140870E+08, 1.266953E+08, 1.405605E+08, 1.557947E+08,
1.725188E+08, 1.908630E+08, 2.109676E+08, 2.329838E+08, 2.570740E+08, 2.834131E+08,
3.121888E+08, 3.436027E+08, 3.778713E+08, 4.152266E+08, 4.559173E+08, 5.002100E+08,
5.483899E+08, 6.007624E+08, 6.576539E+08, 7.194134E+08, 7.864137E+08, 8.590528E+08,
9.377556E+08, 1.022975E+09, 1.115194E+09, 1.214928E+09, 1.322724E+09, 1.439167E+09,
1.564877E+09, 1.700514E+09, 1.846781E+09, 2.004423E+09, 2.174233E+09, 2.357051E+09,
2.553770E+09, 2.765338E+09, 2.992756E+09, 3.237090E+09, 3.499466E+09, 3.781077E+09,
4.083186E+09, 4.407130E+09, 4.754323E+09, 5.126259E+09, 5.524518E+09, 5.950769E+09,
6.406775E+09, 6.894396E+09, 7.415596E+09, 7.972447E+09, 8.567134E+09, 9.201960E+09,
9.879354E+09, 1.060187E+10, 1.137221E+10, 1.219320E+10, 1.306783E+10, 1.399924E+10,
1.499073E+10, 1.604578E+10, 1.716804E+10, 1.836135E+10, 1.962973E+10, 2.097741E+10,
2.240886E+10, 2.392872E+10, 2.554190E+10, 2.725353E+10, 2.906899E+10, 3.099394E+10,
3.303430E+10, 3.519625E+10, 3.748631E+10, 3.991127E+10, 4.247826E+10, 4.519474E+10,
4.806852E+10, 5.110775E+10, 5.432098E+10, 5.771714E+10, 6.130557E+10, 6.509603E+10,
6.909872E+10, 7.332428E+10, 7.778385E+10, 8.248906E+10, 8.745202E+10, 9.268540E+10,
9.820242E+10, 1.040169E+11, 1.101431E+11, 1.165961E+11, 1.233916E+11, 1.305458E+11,
1.380757E+11, 1.459989E+11, 1.543341E+11, 1.631002E+11, 1.723174E+11, 1.820064E+11,
1.921888E+11, 2.028874E+11, 2.141254E+11, 2.259273E+11, 2.383185E+11, 2.513254E+11,
2.649754E+11, 2.792970E+11,
])
# ---------------------- M = 45, I = 1 ---------------------------
M = 45
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[1]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.000000E+00, 1.001788E+00, 1.126881E+00, 1.526206E+00, 2.077149E+00, 2.667730E+00,
3.249956E+00, 3.809254E+00, 4.344827E+00, 4.860660E+00, 5.361676E+00, 5.852264E+00,
6.335855E+00, 6.814950E+00, 7.291287E+00, 7.766034E+00, 8.239959E+00, 8.713552E+00,
9.187121E+00, 9.660856E+00, 1.013487E+01, 1.060924E+01, 1.108399E+01, 1.155914E+01,
1.203472E+01, 1.251071E+01, 1.298713E+01, 1.346397E+01, 1.394124E+01, 1.441895E+01,
1.489709E+01, 1.537567E+01, 1.585471E+01, 1.633422E+01, 1.681420E+01, 1.729468E+01,
1.777567E+01, 1.825719E+01, 1.873927E+01, 1.922193E+01, 1.970520E+01, 2.018910E+01,
2.067367E+01, 2.115894E+01, 2.164494E+01, 2.213171E+01, 2.261929E+01, 2.310771E+01,
2.359701E+01, 2.408724E+01, 2.457844E+01, 2.507064E+01, 2.556390E+01, 2.605825E+01,
2.655374E+01, 2.705042E+01, 2.754832E+01, 2.804751E+01, 2.854802E+01, 2.904991E+01,
2.955321E+01, 3.005797E+01, 3.056425E+01, 3.107208E+01, 3.158152E+01, 3.209261E+01,
3.260540E+01, 3.311994E+01, 3.363627E+01, 3.415443E+01, 3.467448E+01, 3.519645E+01,
3.572040E+01, 3.624636E+01, 3.677439E+01, 3.730452E+01, 3.783680E+01, 3.837127E+01,
3.890798E+01, 3.944696E+01, 3.998826E+01, 4.053192E+01, 4.107798E+01, 4.162648E+01,
4.217746E+01, 4.273095E+01, 4.328701E+01, 4.384566E+01, 4.440694E+01, 4.497089E+01,
4.553755E+01, 4.610696E+01, 4.667914E+01, 4.725414E+01, 4.783199E+01, 4.841272E+01,
4.899637E+01, 4.958297E+01, 5.017256E+01, 5.076516E+01, 5.136081E+01, 5.195955E+01,
5.256139E+01, 5.316638E+01, 5.377454E+01, 5.438591E+01, 5.500051E+01, 5.561837E+01,
5.623952E+01, 5.686400E+01, 5.749182E+01, 5.812302E+01, 5.875762E+01, 5.939565E+01,
6.003713E+01, 6.068210E+01, 6.133059E+01, 6.198260E+01, 6.263818E+01, 6.329734E+01,
6.396011E+01, 6.462652E+01, 6.529658E+01, 6.597033E+01, 6.664778E+01, 6.732897E+01,
6.801391E+01, 6.870262E+01, 6.939513E+01, 7.009147E+01, 7.079164E+01, 7.149568E+01,
7.220361E+01, 7.291544E+01, 7.363121E+01, 7.435092E+01, 7.507461E+01, 7.580228E+01,
7.653397E+01, 7.726969E+01, 7.800946E+01, 7.875330E+01, 7.950123E+01, 8.025328E+01,
8.100945E+01, 8.176978E+01, 8.253427E+01, 8.330295E+01, 8.407583E+01, 8.485294E+01,
8.563429E+01, 8.641990E+01, 8.720979E+01, 8.800398E+01, 8.880249E+01, 8.960532E+01,
9.041251E+01, 9.122406E+01, 9.204000E+01, 9.286034E+01, 9.368510E+01, 9.451430E+01,
9.534795E+01, 9.618607E+01, 9.702868E+01, 9.787579E+01, 9.872743E+01, 9.958360E+01,
1.004443E+02, 1.013096E+02, 1.021795E+02, 1.030540E+02, 1.039331E+02, 1.048168E+02,
1.057052E+02, 1.065983E+02, 1.074960E+02, 1.083984E+02, 1.093056E+02, 1.102175E+02,
1.111341E+02, 1.120555E+02, 1.129816E+02, 1.139126E+02, 1.148484E+02, 1.157890E+02,
1.167344E+02, 1.176847E+02, 1.186398E+02, 1.195998E+02, 1.205648E+02, 1.215346E+02,
1.225094E+02, 1.234891E+02, 1.244738E+02, 1.254635E+02, 1.264581E+02, 1.274578E+02,
1.284624E+02, 1.294721E+02, 1.304869E+02, 1.315067E+02, 1.325316E+02, 1.335616E+02,
1.345967E+02, 1.356369E+02, 1.366822E+02, 1.377327E+02, 1.387884E+02, 1.398492E+02,
1.409153E+02, 1.419865E+02, 1.430630E+02, 1.441447E+02, 1.452317E+02, 1.463239E+02,
1.474214E+02, 1.485242E+02, 1.496323E+02, 1.507458E+02, 1.518645E+02, 1.529887E+02,
1.541181E+02, 1.552530E+02, 1.563932E+02, 1.575389E+02, 1.586900E+02, 1.598465E+02,
1.610084E+02, 1.621758E+02, 1.633487E+02, 1.645271E+02, 1.657110E+02, 1.669004E+02,
1.680953E+02, 1.692957E+02, 1.705017E+02, 1.717133E+02, 1.729305E+02, 1.741532E+02,
1.753815E+02, 1.766155E+02, 1.778551E+02, 1.791004E+02, 1.803513E+02, 1.816078E+02,
1.828701E+02, 1.841380E+02, 1.854117E+02, 1.866910E+02, 1.879761E+02, 1.892670E+02,
1.905636E+02, 1.918659E+02, 1.931741E+02, 1.944880E+02, 1.958078E+02, 1.971333E+02,
1.984647E+02, 1.998019E+02, 2.011450E+02, 2.024939E+02, 2.038487E+02, 2.052094E+02,
2.065760E+02, 2.079485E+02, 2.093269E+02, 2.107112E+02, 2.121014E+02, 2.134977E+02,
2.148998E+02, 2.163080E+02, 2.177221E+02, 2.191422E+02, 2.205683E+02, 2.220004E+02,
2.234386E+02, 2.248827E+02, 2.263329E+02, 2.277892E+02, 2.292515E+02, 2.307199E+02,
2.321943E+02, 2.336749E+02, 2.351615E+02, 2.366543E+02, 2.381531E+02, 2.396581E+02,
2.411692E+02, 2.426864E+02, 2.442098E+02, 2.457394E+02, 2.472751E+02, 2.488169E+02,
2.503650E+02, 2.519192E+02, 2.534797E+02, 2.550463E+02, 2.566191E+02, 2.581982E+02,
2.597835E+02,
])
# ---------------------- M = 45, I = 2 ---------------------------
M = 45
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[1]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
6.000000E+00, 6.029347E+00, 6.728818E+00, 8.168283E+00, 9.865998E+00, 1.164890E+01,
1.346792E+01, 1.530608E+01, 1.715597E+01, 1.901383E+01, 2.087753E+01, 2.274575E+01,
2.461767E+01, 2.649271E+01, 2.837047E+01, 3.025068E+01, 3.213312E+01, 3.401765E+01,
3.590412E+01, 3.779247E+01, 3.968262E+01, 4.157454E+01, 4.346818E+01, 4.536356E+01,
4.726067E+01, 4.915955E+01, 5.106023E+01, 5.296280E+01, 5.486733E+01, 5.677392E+01,
5.868270E+01, 6.059382E+01, 6.250744E+01, 6.442372E+01, 6.634288E+01, 6.826512E+01,
7.019067E+01, 7.211977E+01, 7.405266E+01, 7.598962E+01, 7.793091E+01, 7.987682E+01,
8.182762E+01, 8.378361E+01, 8.574509E+01, 8.771234E+01, 8.968568E+01, 9.166539E+01,
9.365178E+01, 9.564515E+01, 9.764579E+01, 9.965399E+01, 1.016701E+02, 1.036943E+02,
1.057269E+02, 1.077682E+02, 1.098186E+02, 1.118781E+02, 1.139472E+02, 1.160261E+02,
1.181150E+02, 1.202141E+02, 1.223238E+02, 1.244442E+02, 1.265756E+02, 1.287182E+02,
1.308723E+02, 1.330380E+02, 1.352155E+02, 1.374051E+02, 1.396070E+02, 1.418213E+02,
1.440482E+02, 1.462879E+02, 1.485407E+02, 1.508066E+02, 1.530859E+02, 1.553786E+02,
1.576850E+02, 1.600053E+02, 1.623395E+02, 1.646878E+02, 1.670504E+02, 1.694274E+02,
1.718190E+02, 1.742252E+02, 1.766463E+02, 1.790823E+02, 1.815333E+02, 1.839996E+02,
1.864811E+02, 1.889781E+02, 1.914906E+02, 1.940188E+02, 1.965627E+02, 1.991225E+02,
2.016983E+02, 2.042901E+02, 2.068981E+02, 2.095224E+02, 2.121631E+02, 2.148202E+02,
2.174939E+02, 2.201843E+02, 2.228913E+02, 2.256153E+02, 2.283561E+02, 2.311140E+02,
2.338889E+02, 2.366811E+02, 2.394905E+02, 2.423172E+02, 2.451614E+02, 2.480230E+02,
2.509023E+02, 2.537992E+02, 2.567138E+02, 2.596462E+02, 2.625966E+02, 2.655649E+02,
2.685512E+02, 2.715556E+02, 2.745782E+02, 2.776191E+02, 2.806782E+02, 2.837558E+02,
2.868518E+02, 2.899664E+02, 2.930995E+02, 2.962513E+02, 2.994219E+02, 3.026112E+02,
3.058195E+02, 3.090466E+02, 3.122928E+02, 3.155580E+02, 3.188423E+02, 3.221459E+02,
3.254687E+02, 3.288109E+02, 3.321724E+02, 3.355534E+02, 3.389539E+02, 3.423740E+02,
3.458138E+02, 3.492732E+02, 3.527525E+02, 3.562516E+02, 3.597706E+02, 3.633095E+02,
3.668685E+02, 3.704476E+02, 3.740468E+02, 3.776663E+02, 3.813061E+02, 3.849662E+02,
3.886467E+02, 3.923476E+02, 3.960692E+02, 3.998113E+02, 4.035740E+02, 4.073576E+02,
4.111618E+02, 4.149870E+02, 4.188330E+02, 4.227001E+02, 4.265882E+02, 4.304974E+02,
4.344277E+02, 4.383793E+02, 4.423522E+02, 4.463464E+02, 4.503621E+02, 4.543992E+02,
4.584579E+02, 4.625383E+02, 4.666403E+02, 4.707640E+02, 4.749095E+02, 4.790770E+02,
4.832663E+02, 4.874777E+02, 4.917111E+02, 4.959667E+02, 5.002444E+02, 5.045444E+02,
5.088667E+02, 5.132115E+02, 5.175787E+02, 5.219684E+02, 5.263806E+02, 5.308156E+02,
5.352732E+02, 5.397537E+02, 5.442570E+02, 5.487832E+02, 5.533323E+02, 5.579046E+02,
5.624999E+02, 5.671184E+02, 5.717602E+02, 5.764253E+02, 5.811137E+02, 5.858256E+02,
5.905610E+02, 5.953200E+02, 6.001026E+02, 6.049090E+02, 6.097391E+02, 6.145930E+02,
6.194709E+02, 6.243727E+02, 6.292986E+02, 6.342486E+02, 6.392228E+02, 6.442212E+02,
6.492439E+02, 6.542909E+02, 6.593625E+02, 6.644585E+02, 6.695791E+02, 6.747243E+02,
6.798943E+02, 6.850890E+02, 6.903085E+02, 6.955530E+02, 7.008224E+02, 7.061168E+02,
7.114364E+02, 7.167811E+02, 7.221510E+02, 7.275463E+02, 7.329669E+02, 7.384129E+02,
7.438844E+02, 7.493815E+02, 7.549042E+02, 7.604525E+02, 7.660266E+02, 7.716266E+02,
7.772523E+02, 7.829041E+02, 7.885818E+02, 7.942856E+02, 8.000155E+02, 8.057716E+02,
8.115539E+02, 8.173626E+02, 8.231976E+02, 8.290591E+02, 8.349470E+02, 8.408615E+02,
8.468026E+02, 8.527704E+02, 8.587650E+02, 8.647863E+02, 8.708345E+02, 8.769095E+02,
8.830116E+02, 8.891406E+02, 8.952968E+02, 9.014801E+02, 9.076906E+02, 9.139283E+02,
9.201934E+02, 9.264858E+02, 9.328056E+02, 9.391529E+02, 9.455278E+02, 9.519302E+02,
9.583602E+02, 9.648179E+02, 9.713034E+02, 9.778167E+02, 9.843578E+02, 9.909268E+02,
9.975237E+02, 1.004149E+03, 1.010802E+03, 1.017483E+03, 1.024192E+03, 1.030929E+03,
1.037695E+03, 1.044489E+03, 1.051311E+03, 1.058162E+03, 1.065041E+03, 1.071948E+03,
1.078884E+03, 1.085849E+03, 1.092842E+03, 1.099863E+03, 1.106914E+03, 1.113993E+03,
1.121101E+03, 1.128237E+03, 1.135403E+03, 1.142597E+03, 1.149821E+03, 1.157073E+03,
1.164354E+03,
])
# ---------------------- M = 46, I = 1 ---------------------------
M = 46
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.290560E+00, 1.735586E+01, 3.438114E+01, 5.141645E+01, 6.846079E+01, 8.551397E+01,
1.025760E+02, 1.196469E+02, 1.367275E+02, 1.538201E+02, 1.709292E+02, 1.880624E+02,
2.052309E+02, 2.224487E+02, 2.397327E+02, 2.571016E+02, 2.745752E+02, 2.921739E+02,
3.099177E+02, 3.278264E+02, 3.459187E+02, 3.642124E+02, 3.827237E+02, 4.014681E+02,
4.204596E+02, 4.397109E+02, 4.592337E+02, 4.790386E+02, 4.991353E+02, 5.195323E+02,
5.402377E+02, 5.612585E+02, 5.826011E+02, 6.042715E+02, 6.262748E+02, 6.486160E+02,
6.712993E+02, 6.943287E+02, 7.177078E+02, 7.414400E+02, 7.655282E+02, 7.899751E+02,
8.147835E+02, 8.399554E+02, 8.654931E+02, 8.913986E+02, 9.176737E+02, 9.443201E+02,
9.713393E+02, 9.987328E+02, 1.026502E+03, 1.054648E+03, 1.083173E+03, 1.112076E+03,
1.141360E+03, 1.171026E+03, 1.201073E+03, 1.231504E+03, 1.262319E+03, 1.293518E+03,
1.325104E+03, 1.357075E+03, 1.389434E+03, 1.422180E+03, 1.455315E+03, 1.488838E+03,
1.522751E+03, 1.557055E+03, 1.591749E+03, 1.626834E+03, 1.662311E+03, 1.698180E+03,
1.734442E+03, 1.771097E+03, 1.808146E+03, 1.845590E+03, 1.883427E+03, 1.921660E+03,
1.960288E+03, 1.999313E+03, 2.038733E+03, 2.078551E+03, 2.118765E+03, 2.159377E+03,
2.200387E+03, 2.241795E+03, 2.283602E+03, 2.325808E+03, 2.368413E+03, 2.411418E+03,
2.454824E+03, 2.498630E+03, 2.542836E+03, 2.587444E+03, 2.632454E+03, 2.677865E+03,
2.723679E+03, 2.769895E+03, 2.816514E+03, 2.863536E+03, 2.910962E+03, 2.958792E+03,
3.007026E+03, 3.055665E+03, 3.104709E+03, 3.154158E+03, 3.204012E+03, 3.254272E+03,
3.304939E+03, 3.356012E+03, 3.407492E+03, 3.459379E+03, 3.511673E+03, 3.564376E+03,
3.617486E+03, 3.671005E+03, 3.724933E+03, 3.779269E+03, 3.834015E+03, 3.889171E+03,
3.944736E+03, 4.000712E+03, 4.057099E+03, 4.113896E+03, 4.171105E+03, 4.228725E+03,
4.286756E+03, 4.345200E+03, 4.404057E+03, 4.463326E+03, 4.523008E+03, 4.583104E+03,
4.643613E+03, 4.704536E+03, 4.765873E+03, 4.827625E+03, 4.889792E+03, 4.952374E+03,
5.015371E+03, 5.078784E+03, 5.142613E+03, 5.206859E+03, 5.271521E+03, 5.336600E+03,
5.402097E+03, 5.468011E+03, 5.534343E+03, 5.601093E+03, 5.668261E+03, 5.735848E+03,
5.803855E+03, 5.872280E+03, 5.941126E+03, 6.010391E+03, 6.080076E+03, 6.150183E+03,
6.220709E+03, 6.291658E+03, 6.363027E+03, 6.434819E+03, 6.507032E+03, 6.579668E+03,
6.652726E+03, 6.726207E+03, 6.800112E+03, 6.874440E+03, 6.949192E+03, 7.024368E+03,
7.099969E+03, 7.175994E+03, 7.252444E+03, 7.329320E+03, 7.406621E+03, 7.484349E+03,
7.562502E+03, 7.641082E+03,
])
# ---------------------- M = 46, I = 2 ---------------------------
M = 46
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.301900E+00, 1.763255E+01, 3.493474E+01, 5.224710E+01, 6.956862E+01, 8.689912E+01,
1.042386E+02, 1.215871E+02, 1.389456E+02, 1.563165E+02, 1.737047E+02, 1.911184E+02,
2.085692E+02, 2.260721E+02, 2.436447E+02, 2.613063E+02, 2.790777E+02, 2.969798E+02,
3.150333E+02, 3.332584E+02, 3.516744E+02, 3.702993E+02, 3.891500E+02, 4.082419E+02,
4.275893E+02, 4.472052E+02, 4.671014E+02, 4.872886E+02, 5.077767E+02, 5.285742E+02,
5.496893E+02, 5.711290E+02, 5.928998E+02, 6.150077E+02, 6.374580E+02, 6.602555E+02,
6.834045E+02, 7.069091E+02, 7.307729E+02, 7.549992E+02, 7.795910E+02, 8.045510E+02,
8.298819E+02, 8.555859E+02, 8.816653E+02, 9.081219E+02, 9.349576E+02, 9.621742E+02,
9.897732E+02, 1.017756E+03, 1.046124E+03, 1.074879E+03, 1.104021E+03, 1.133552E+03,
1.163473E+03, 1.193785E+03, 1.224489E+03, 1.255586E+03, 1.287076E+03, 1.318961E+03,
1.351240E+03, 1.383916E+03, 1.416989E+03, 1.450458E+03, 1.484326E+03, 1.518592E+03,
1.553258E+03, 1.588323E+03, 1.623789E+03, 1.659656E+03, 1.695923E+03, 1.732593E+03,
1.769666E+03, 1.807141E+03, 1.845020E+03, 1.883302E+03, 1.921989E+03, 1.961081E+03,
2.000577E+03, 2.040480E+03, 2.080788E+03, 2.121503E+03, 2.162625E+03, 2.204154E+03,
2.246091E+03, 2.288436E+03, 2.331189E+03, 2.374352E+03, 2.417923E+03, 2.461904E+03,
2.506295E+03, 2.551096E+03, 2.596308E+03, 2.641930E+03, 2.687965E+03, 2.734411E+03,
2.781269E+03, 2.828539E+03, 2.876222E+03, 2.924319E+03, 2.972829E+03, 3.021752E+03,
3.071090E+03, 3.120842E+03, 3.171009E+03, 3.221591E+03, 3.272589E+03, 3.324002E+03,
3.375832E+03, 3.428078E+03, 3.480740E+03, 3.533820E+03, 3.587317E+03, 3.641232E+03,
3.695565E+03, 3.750317E+03, 3.805487E+03, 3.861076E+03, 3.917084E+03, 3.973512E+03,
4.030360E+03, 4.087629E+03, 4.145318E+03, 4.203427E+03, 4.261959E+03, 4.320911E+03,
4.380286E+03, 4.440082E+03, 4.500301E+03, 4.560943E+03, 4.622008E+03, 4.683497E+03,
4.745409E+03, 4.807745E+03, 4.870506E+03, 4.933691E+03, 4.997301E+03, 5.061336E+03,
5.125797E+03, 5.190684E+03, 5.255997E+03, 5.321737E+03, 5.387903E+03, 5.454497E+03,
5.521518E+03, 5.588967E+03, 5.656843E+03, 5.725148E+03, 5.793882E+03, 5.863045E+03,
5.932637E+03, 6.002659E+03, 6.073110E+03, 6.143992E+03, 6.215304E+03, 6.287047E+03,
6.359221E+03, 6.431827E+03, 6.504864E+03, 6.578333E+03, 6.652235E+03, 6.726569E+03,
6.801336E+03, 6.876537E+03, 6.952171E+03, 7.028239E+03, 7.104741E+03, 7.181677E+03,
7.259049E+03, 7.336855E+03, 7.415097E+03, 7.493774E+03, 7.572888E+03, 7.652438E+03,
7.732424E+03, 7.812847E+03,
])
# ---------------------- M = 46, I = 3 ---------------------------
M = 46
I = 3
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
2.665010E+00, 3.673026E+01, 7.280106E+01, 1.088928E+02, 1.450036E+02, 1.811332E+02,
2.172814E+02, 2.534487E+02, 2.896373E+02, 3.258533E+02, 3.621084E+02, 3.984219E+02,
4.348208E+02, 4.713391E+02, 5.080160E+02, 5.448948E+02, 5.820205E+02, 6.194387E+02,
6.571942E+02, 6.953301E+02, 7.338872E+02, 7.729036E+02, 8.124146E+02, 8.524529E+02,
8.930479E+02, 9.342266E+02, 9.760135E+02, 1.018431E+03, 1.061498E+03, 1.105234E+03,
1.149654E+03, 1.194773E+03, 1.240604E+03, 1.287160E+03, 1.334451E+03, 1.382487E+03,
1.431277E+03, 1.480828E+03, 1.531149E+03, 1.582246E+03, 1.634124E+03, 1.686790E+03,
1.740249E+03, 1.794505E+03, 1.849563E+03, 1.905427E+03, 1.962100E+03, 2.019586E+03,
2.077888E+03, 2.137009E+03, 2.196951E+03, 2.257718E+03, 2.319312E+03, 2.381735E+03,
2.444990E+03, 2.509077E+03, 2.574000E+03, 2.639760E+03, 2.706358E+03, 2.773797E+03,
2.842078E+03, 2.911202E+03, 2.981172E+03, 3.051987E+03, 3.123651E+03, 3.196163E+03,
3.269525E+03, 3.343739E+03, 3.418805E+03, 3.494725E+03, 3.571500E+03, 3.649130E+03,
3.727618E+03, 3.806963E+03, 3.887167E+03, 3.968231E+03, 4.050155E+03, 4.132942E+03,
4.216590E+03, 4.301103E+03, 4.386479E+03, 4.472721E+03, 4.559829E+03, 4.647804E+03,
4.736646E+03, 4.826357E+03, 4.916938E+03, 5.008388E+03, 5.100710E+03, 5.193903E+03,
5.287969E+03, 5.382908E+03, 5.478720E+03, 5.575408E+03, 5.672971E+03, 5.771411E+03,
5.870727E+03, 5.970921E+03, 6.071993E+03, 6.173945E+03, 6.276777E+03, 6.380489E+03,
6.485082E+03, 6.590557E+03, 6.696916E+03, 6.804157E+03, 6.912283E+03, 7.021293E+03,
7.131189E+03, 7.241971E+03, 7.353640E+03, 7.466197E+03, 7.579641E+03, 7.693975E+03,
7.809198E+03, 7.925312E+03, 8.042316E+03, 8.160212E+03, 8.279001E+03, 8.398682E+03,
8.519257E+03, 8.640727E+03, 8.763091E+03, 8.886351E+03, 9.010508E+03, 9.135561E+03,
9.261512E+03, 9.388361E+03, 9.516109E+03, 9.644757E+03, 9.774306E+03, 9.904755E+03,
1.003611E+04, 1.016836E+04, 1.030151E+04, 1.043557E+04, 1.057054E+04, 1.070641E+04,
1.084318E+04, 1.098086E+04, 1.111945E+04, 1.125894E+04, 1.139935E+04, 1.154066E+04,
1.168288E+04, 1.182601E+04, 1.197005E+04, 1.211501E+04, 1.226087E+04, 1.240765E+04,
1.255535E+04, 1.270395E+04, 1.285347E+04, 1.300391E+04, 1.315526E+04, 1.330753E+04,
1.346072E+04, 1.361482E+04, 1.376984E+04, 1.392579E+04, 1.408265E+04, 1.424043E+04,
1.439914E+04, 1.455876E+04, 1.471931E+04, 1.488078E+04, 1.504318E+04, 1.520650E+04,
1.537074E+04, 1.553591E+04, 1.570201E+04, 1.586903E+04, 1.603699E+04, 1.620587E+04,
1.637567E+04, 1.654641E+04,
])
# ---------------------- M = 46, I = 4 ---------------------------
M = 46
I = 4
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
5.185410E+00, 6.999021E+01, 1.386585E+02, 2.073673E+02, 2.761123E+02, 3.448931E+02,
4.137093E+02, 4.825616E+02, 5.514533E+02, 6.203936E+02, 6.894014E+02, 7.585087E+02,
8.277606E+02, 8.972156E+02, 9.669423E+02, 1.037017E+03, 1.107521E+03, 1.178536E+03,
1.250145E+03, 1.322428E+03, 1.395459E+03, 1.469311E+03, 1.544051E+03, 1.619739E+03,
1.696433E+03, 1.774184E+03, 1.853038E+03, 1.933040E+03, 2.014227E+03, 2.096634E+03,
2.180293E+03, 2.265233E+03, 2.351479E+03, 2.439054E+03, 2.527982E+03, 2.618279E+03,
2.709964E+03, 2.803053E+03, 2.897560E+03, 2.993499E+03, 3.090881E+03, 3.189718E+03,
3.290019E+03, 3.391794E+03, 3.495052E+03, 3.599800E+03, 3.706046E+03, 3.813797E+03,
3.923059E+03, 4.033837E+03, 4.146138E+03, 4.259966E+03, 4.375326E+03, 4.492223E+03,
4.610661E+03, 4.730643E+03, 4.852175E+03, 4.975258E+03, 5.099898E+03, 5.226096E+03,
5.353856E+03, 5.483181E+03, 5.614074E+03, 5.746537E+03, 5.880573E+03, 6.016185E+03,
6.153375E+03, 6.292145E+03, 6.432497E+03, 6.574434E+03, 6.717958E+03, 6.863070E+03,
7.009774E+03, 7.158070E+03, 7.307961E+03, 7.459448E+03, 7.612533E+03, 7.767219E+03,
7.923506E+03, 8.081397E+03, 8.240893E+03, 8.401995E+03, 8.564707E+03, 8.729028E+03,
8.894960E+03, 9.062506E+03, 9.231667E+03, 9.402443E+03, 9.574837E+03, 9.748851E+03,
9.924485E+03, 1.010174E+04, 1.028062E+04, 1.046112E+04, 1.064325E+04, 1.082701E+04,
1.101240E+04, 1.119942E+04, 1.138807E+04, 1.157835E+04, 1.177027E+04, 1.196382E+04,
1.215901E+04, 1.235584E+04, 1.255431E+04, 1.275442E+04, 1.295617E+04, 1.315956E+04,
1.336460E+04, 1.357129E+04, 1.377962E+04, 1.398961E+04, 1.420124E+04, 1.441453E+04,
1.462946E+04, 1.484606E+04, 1.506430E+04, 1.528421E+04, 1.550577E+04, 1.572899E+04,
1.595387E+04, 1.618041E+04, 1.640861E+04, 1.663848E+04, 1.687002E+04, 1.710322E+04,
1.733808E+04, 1.757462E+04, 1.781283E+04, 1.805271E+04, 1.829426E+04, 1.853748E+04,
1.878238E+04, 1.902896E+04, 1.927721E+04, 1.952715E+04, 1.977876E+04, 2.003205E+04,
2.028703E+04, 2.054369E+04, 2.080203E+04, 2.106207E+04, 2.132378E+04, 2.158719E+04,
2.185229E+04, 2.211908E+04, 2.238756E+04, 2.265773E+04, 2.292960E+04, 2.320316E+04,
2.347842E+04, 2.375538E+04, 2.403404E+04, 2.431440E+04, 2.459646E+04, 2.488023E+04,
2.516569E+04, 2.545287E+04, 2.574175E+04, 2.603234E+04, 2.632464E+04, 2.661864E+04,
2.691436E+04, 2.721180E+04, 2.751094E+04, 2.781181E+04, 2.811438E+04, 2.841868E+04,
2.872470E+04, 2.903243E+04, 2.934189E+04, 2.965306E+04, 2.996597E+04, 3.028059E+04,
3.059695E+04, 3.091503E+04,
])
# ---------------------- M = 47, I = 1 ---------------------------
M = 47
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.414270E+00, 1.057361E+02, 2.984678E+02, 5.479928E+02, 8.436907E+02, 1.180696E+03,
1.558672E+03, 1.981177E+03, 2.454584E+03, 2.987094E+03, 3.588121E+03, 4.268040E+03,
5.038147E+03, 5.910737E+03, 6.899230E+03, 8.018317E+03, 9.284110E+03, 1.071430E+04,
1.232829E+04, 1.414738E+04, 1.619491E+04, 1.849642E+04, 2.107982E+04, 2.397557E+04,
2.721685E+04, 3.083979E+04, 3.488362E+04, 3.939089E+04, 4.440770E+04, 4.998392E+04,
5.617341E+04, 6.303426E+04, 7.062902E+04, 7.884530E+04, 8.807839E+04, 9.825772E+04,
1.094663E+05, 1.217930E+05, 1.353330E+05, 1.501882E+05, 1.664674E+05, 1.842866E+05,
2.037697E+05, 2.250484E+05, 2.482633E+05, 2.735634E+05, 3.011073E+05, 3.310633E+05,
3.636098E+05, 3.989359E+05, 4.372419E+05, 4.787394E+05, 5.236525E+05, 5.722175E+05,
6.246841E+05, 6.813156E+05, 7.423894E+05, 8.081977E+05, 8.790482E+05, 9.552643E+05,
1.037186E+06, 1.125171E+06, 1.219593E+06, 1.320847E+06, 1.429344E+06, 1.545517E+06,
1.669818E+06, 1.802721E+06, 1.944721E+06, 2.096336E+06, 2.258109E+06, 2.430603E+06,
2.614408E+06, 2.810141E+06, 3.018444E+06, 3.239985E+06, 3.475461E+06, 3.725598E+06,
3.991152E+06, 4.272907E+06, 4.571682E+06, 4.888326E+06, 5.223722E+06, 5.578785E+06,
5.954468E+06, 6.351757E+06, 6.771677E+06, 7.215291E+06, 7.683698E+06, 8.178039E+06,
8.699497E+06, 9.249293E+06, 9.828694E+06, 1.043901E+07, 1.108160E+07, 1.175785E+07,
1.246923E+07, 1.321721E+07, 1.400336E+07, 1.482927E+07, 1.569657E+07, 1.660698E+07,
1.756225E+07, 1.856419E+07, 1.961465E+07, 2.071558E+07, 2.186893E+07, 2.307677E+07,
2.434119E+07, 2.566435E+07, 2.704850E+07, 2.849591E+07, 3.000895E+07, 3.159005E+07,
3.324171E+07, 3.496649E+07, 3.676703E+07, 3.864605E+07, 4.060633E+07, 4.265074E+07,
4.478221E+07, 4.700377E+07, 4.931851E+07, 5.172961E+07, 5.424035E+07, 5.685405E+07,
5.957418E+07, 6.240423E+07, 6.534783E+07, 6.840867E+07, 7.159056E+07, 7.489738E+07,
7.833310E+07, 8.190183E+07, 8.560772E+07, 8.945507E+07, 9.344824E+07, 9.759174E+07,
1.018901E+08, 1.063481E+08, 1.109706E+08, 1.157623E+08, 1.207284E+08, 1.258740E+08,
1.312043E+08, 1.367248E+08, 1.424409E+08, 1.483582E+08, 1.544824E+08, 1.608195E+08,
1.673754E+08, 1.741563E+08, 1.811683E+08, 1.884179E+08, 1.959115E+08, 2.036558E+08,
2.116577E+08, 2.199239E+08, 2.284616E+08, 2.372780E+08, 2.463805E+08, 2.557765E+08,
2.654736E+08, 2.754798E+08, 2.858029E+08, 2.964510E+08, 3.074325E+08, 3.187557E+08,
3.304293E+08, 3.424619E+08, 3.548625E+08, 3.676402E+08, 3.808043E+08, 3.943641E+08,
4.083293E+08, 4.227097E+08,
])
# ---------------------- M = 48, I = 1 ---------------------------
M = 48
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
2.147898E+01, 3.996775E+02, 7.982281E+02, 1.204932E+03, 1.643306E+03, 2.140834E+03,
2.722097E+03, 3.410126E+03, 4.228639E+03, 5.203092E+03, 6.361663E+03, 7.735468E+03,
9.359297E+03, 1.127186E+04, 1.351617E+04, 1.614009E+04, 1.919639E+04, 2.274374E+04,
2.684666E+04, 3.157632E+04, 3.701089E+04, 4.323629E+04, 5.034661E+04, 5.844492E+04,
6.764383E+04, 7.806626E+04, 8.984612E+04, 1.031293E+05, 1.180743E+05, 1.348531E+05,
1.536525E+05, 1.746748E+05, 1.981385E+05, 2.242802E+05, 2.533549E+05, 2.856378E+05,
3.214253E+05, 3.610363E+05, 4.048136E+05, 4.531253E+05, 5.063665E+05, 5.649605E+05,
6.293605E+05, 7.000516E+05, 7.775523E+05, 8.624165E+05, 9.552351E+05, 1.056639E+06,
1.167298E+06, 1.287929E+06, 1.419291E+06, 1.562193E+06, 1.717494E+06, 1.886106E+06,
2.068996E+06, 2.267189E+06, 2.481773E+06, 2.713896E+06, 2.964776E+06, 3.235699E+06,
3.528026E+06, 3.843193E+06, 4.182715E+06, 4.548192E+06, 4.941312E+06, 5.363849E+06,
5.817677E+06, 6.304765E+06, 6.827188E+06, 7.387122E+06, 7.986861E+06, 8.628813E+06,
9.315504E+06, 1.004959E+07, 1.083385E+07, 1.167121E+07, 1.256472E+07, 1.351760E+07,
1.453319E+07, 1.561502E+07, 1.676676E+07, 1.799226E+07, 1.929555E+07, 2.068082E+07,
2.215247E+07, 2.371510E+07, 2.537348E+07, 2.713262E+07, 2.899773E+07, 3.097426E+07,
3.306787E+07, 3.528449E+07, 3.763024E+07, 4.011156E+07, 4.273513E+07, 4.550790E+07,
4.843709E+07, 5.153021E+07, 5.479511E+07, 5.823992E+07, 6.187306E+07, 6.570334E+07,
6.973986E+07, 7.399210E+07, 7.846990E+07, 8.318344E+07, 8.814330E+07, 9.336047E+07,
9.884634E+07, 1.046127E+08, 1.106718E+08, 1.170363E+08, 1.237193E+08, 1.307344E+08,
1.380958E+08, 1.458179E+08, 1.539159E+08, 1.624053E+08, 1.713024E+08, 1.806237E+08,
1.903866E+08, 2.006089E+08, 2.113091E+08, 2.225062E+08, 2.342199E+08, 2.464705E+08,
2.592791E+08, 2.726673E+08, 2.866575E+08, 3.012728E+08, 3.165370E+08, 3.324747E+08,
3.491113E+08, 3.664730E+08, 3.845867E+08, 4.034802E+08, 4.231821E+08, 4.437221E+08,
4.651304E+08, 4.874384E+08, 5.106785E+08, 5.348838E+08, 5.600887E+08, 5.863282E+08,
6.136387E+08, 6.420574E+08, 6.716228E+08, 7.023745E+08, 7.343529E+08, 7.675998E+08,
8.021584E+08, 8.380725E+08, 8.753878E+08, 9.141511E+08, 9.544098E+08, 9.962137E+08,
1.039613E+09, 1.084660E+09, 1.131409E+09, 1.179913E+09, 1.230230E+09, 1.282416E+09,
1.336532E+09, 1.392638E+09, 1.450797E+09, 1.511073E+09, 1.573532E+09, 1.638241E+09,
1.705270E+09, 1.774690E+09, 1.846572E+09, 1.920993E+09, 1.998028E+09, 2.077756E+09,
2.160258E+09, 2.245616E+09,
])
# ---------------------- M = 48, I = 2 ---------------------------
M = 48
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.010601E+01, 1.888698E+02, 3.772493E+02, 5.694819E+02, 7.766830E+02, 1.011842E+03,
1.286579E+03, 1.611778E+03, 1.998651E+03, 2.459229E+03, 3.006830E+03, 3.656162E+03,
4.423668E+03, 5.327646E+03, 6.388423E+03, 7.628619E+03, 9.073184E+03, 1.074984E+04,
1.268909E+04, 1.492457E+04, 1.749322E+04, 2.043566E+04, 2.379635E+04, 2.762402E+04,
3.197188E+04, 3.689804E+04, 4.246576E+04, 4.874404E+04, 5.580776E+04, 6.373828E+04,
7.262379E+04, 8.255991E+04, 9.364995E+04, 1.060058E+05, 1.197478E+05, 1.350062E+05,
1.519210E+05, 1.706431E+05, 1.913342E+05, 2.141685E+05, 2.393327E+05, 2.670268E+05,
2.974652E+05, 3.308769E+05, 3.675071E+05, 4.076176E+05, 4.514877E+05, 4.994153E+05,
5.517179E+05, 6.087329E+05, 6.708202E+05, 7.383616E+05, 8.117633E+05, 8.914563E+05,
9.778976E+05, 1.071572E+06, 1.172993E+06, 1.282703E+06, 1.401279E+06, 1.529328E+06,
1.667493E+06, 1.816453E+06, 1.976924E+06, 2.149662E+06, 2.335465E+06, 2.535172E+06,
2.749668E+06, 2.979884E+06, 3.226799E+06, 3.491445E+06, 3.774903E+06, 4.078312E+06,
4.402866E+06, 4.749820E+06, 5.120489E+06, 5.516252E+06, 5.938556E+06, 6.388917E+06,
6.868920E+06, 7.380229E+06, 7.924578E+06, 8.503790E+06, 9.119763E+06, 9.774486E+06,
1.047004E+07, 1.120858E+07, 1.199238E+07, 1.282380E+07, 1.370531E+07, 1.463948E+07,
1.562898E+07, 1.667661E+07, 1.778528E+07, 1.895802E+07, 2.019800E+07, 2.150848E+07,
2.289289E+07, 2.435479E+07, 2.589786E+07, 2.752596E+07, 2.924309E+07, 3.105337E+07,
3.296113E+07, 3.497084E+07, 3.708715E+07, 3.931488E+07, 4.165904E+07, 4.412480E+07,
4.671754E+07, 4.944286E+07, 5.230652E+07, 5.531451E+07, 5.847304E+07, 6.178856E+07,
6.526767E+07, 6.891730E+07, 7.274457E+07, 7.675684E+07, 8.096177E+07, 8.536722E+07,
8.998134E+07, 9.481260E+07, 9.986970E+07, 1.051616E+08, 1.106977E+08, 1.164876E+08,
1.225411E+08, 1.288686E+08, 1.354806E+08, 1.423880E+08, 1.496021E+08, 1.571345E+08,
1.649972E+08, 1.732026E+08, 1.817634E+08, 1.906927E+08, 2.000042E+08, 2.097116E+08,
2.198294E+08, 2.303725E+08, 2.413560E+08, 2.527958E+08, 2.647078E+08, 2.771089E+08,
2.900161E+08, 3.034471E+08, 3.174201E+08, 3.319536E+08, 3.470669E+08, 3.627796E+08,
3.791123E+08, 3.960856E+08, 4.137211E+08, 4.320410E+08, 4.510675E+08, 4.708244E+08,
4.913352E+08, 5.126249E+08, 5.347184E+08, 5.576418E+08, 5.814215E+08, 6.060851E+08,
6.316606E+08, 6.581766E+08, 6.856628E+08, 7.141496E+08, 7.436678E+08, 7.742496E+08,
8.059274E+08, 8.387350E+08, 8.727071E+08, 9.078783E+08, 9.442853E+08, 9.819650E+08,
1.020955E+09, 1.061295E+09,
])
# ---------------------- M = 49, I = 1 ---------------------------
M = 49
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[0]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.721481E+02, 1.484065E+04, 4.195358E+04, 7.716245E+04, 1.194582E+05, 1.691203E+05,
2.272411E+05, 2.954664E+05, 3.758697E+05, 4.709005E+05, 5.833741E+05, 7.164874E+05,
8.738508E+05, 1.059529E+06, 1.278091E+06, 1.534657E+06, 1.834964E+06, 2.185421E+06,
2.593175E+06, 3.066183E+06, 3.613284E+06, 4.244280E+06, 4.970014E+06, 5.802462E+06,
6.754827E+06, 7.841634E+06, 9.078840E+06, 1.048393E+07, 1.207606E+07, 1.387613E+07,
1.590698E+07, 1.819346E+07, 2.076262E+07, 2.364381E+07, 2.686889E+07, 3.047235E+07,
3.449152E+07, 3.896669E+07, 4.394135E+07, 4.946237E+07, 5.558021E+07, 6.234908E+07,
6.982725E+07, 7.807722E+07, 8.716595E+07, 9.716517E+07, 1.081516E+08, 1.202071E+08,
1.334193E+08, 1.478815E+08, 1.636932E+08, 1.809603E+08, 1.997955E+08, 2.203186E+08,
2.426568E+08, 2.669453E+08, 2.933272E+08, 3.219545E+08, 3.529879E+08, 3.865976E+08,
4.229636E+08, 4.622762E+08, 5.047363E+08, 5.505561E+08, 5.999592E+08, 6.531817E+08,
7.104720E+08, 7.720921E+08, 8.383173E+08, 9.094375E+08, 9.857572E+08, 1.067597E+09,
1.155292E+09, 1.249196E+09, 1.349679E+09, 1.457130E+09, 1.571954E+09, 1.694580E+09,
1.825452E+09, 1.965038E+09, 2.113827E+09, 2.272331E+09, 2.441083E+09, 2.620643E+09,
2.811592E+09, 3.014541E+09, 3.230126E+09, 3.459007E+09, 3.701878E+09, 3.959458E+09,
4.232500E+09, 4.521784E+09, 4.828127E+09, 5.152375E+09, 5.495412E+09, 5.858154E+09,
6.241558E+09, 6.646615E+09, 7.074357E+09, 7.525854E+09, 8.002221E+09, 8.504613E+09,
9.034227E+09, 9.592310E+09, 1.018015E+10, 1.079909E+10, 1.145051E+10, 1.213586E+10,
1.285662E+10, 1.361435E+10, 1.441063E+10, 1.524712E+10, 1.612554E+10, 1.704766E+10,
1.801533E+10, 1.903042E+10, 2.009491E+10, 2.121083E+10, 2.238027E+10, 2.360541E+10,
2.488848E+10, 2.623178E+10, 2.763772E+10, 2.910876E+10, 3.064744E+10, 3.225639E+10,
3.393831E+10, 3.569601E+10, 3.753238E+10, 3.945037E+10, 4.145306E+10, 4.354361E+10,
4.572526E+10, 4.800138E+10, 5.037543E+10, 5.285095E+10, 5.543162E+10, 5.812120E+10,
6.092359E+10, 6.384278E+10, 6.688289E+10, 7.004814E+10, 7.334289E+10, 7.677162E+10,
8.033894E+10, 8.404958E+10, 8.790839E+10, 9.192039E+10, 9.609072E+10, 1.004247E+11,
1.049276E+11, 1.096052E+11, 1.144631E+11, 1.195073E+11, 1.247437E+11, 1.301785E+11,
1.358181E+11, 1.416690E+11, 1.477380E+11, 1.540319E+11, 1.605577E+11, 1.673227E+11,
1.743343E+11, 1.816000E+11, 1.891278E+11, 1.969255E+11, 2.050014E+11, 2.133638E+11,
2.220214E+11, 2.309830E+11, 2.402576E+11, 2.498545E+11, 2.597830E+11, 2.700530E+11,
2.806744E+11, 2.916573E+11, 3.030122E+11, 3.147497E+11, 3.268807E+11, 3.394164E+11,
3.523683E+11, 3.657479E+11, 3.795672E+11, 3.938387E+11, 4.085747E+11, 4.237879E+11,
4.394915E+11, 4.556990E+11, 4.724240E+11, 4.896804E+11, 5.074825E+11, 5.258451E+11,
5.447830E+11, 5.643114E+11, 5.844460E+11, 6.052027E+11, 6.265977E+11, 6.486480E+11,
6.713700E+11, 6.947814E+11, 7.188998E+11, 7.437436E+11, 7.693308E+11, 7.956805E+11,
8.228118E+11, 8.507446E+11, 8.794988E+11, 9.090947E+11, 9.395534E+11, 9.708961E+11,
1.003145E+12, 1.036321E+12, 1.070448E+12, 1.105549E+12, 1.141647E+12, 1.178766E+12,
1.216931E+12, 1.256166E+12, 1.296498E+12, 1.337952E+12, 1.380554E+12, 1.424331E+12,
1.469312E+12, 1.515524E+12, 1.562995E+12, 1.611755E+12, 1.661833E+12, 1.713259E+12,
1.766065E+12, 1.820282E+12, 1.875941E+12, 1.933075E+12, 1.991718E+12, 2.051902E+12,
2.113663E+12, 2.177035E+12, 2.242055E+12, 2.308758E+12, 2.377181E+12, 2.447362E+12,
2.519339E+12, 2.593152E+12, 2.668840E+12, 2.746443E+12, 2.826002E+12, 2.907559E+12,
2.991157E+12, 3.076840E+12, 3.164651E+12, 3.254635E+12, 3.346837E+12,
])
# ---------------------- M = 49, I = 2 ---------------------------
M = 49
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[0]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
3.536788E+02, 3.051435E+04, 8.626393E+04, 1.586606E+05, 2.456296E+05, 3.477452E+05,
4.672541E+05, 6.075406E+05, 7.728694E+05, 9.682784E+05, 1.199558E+06, 1.473286E+06,
1.796889E+06, 2.178729E+06, 2.628202E+06, 3.155848E+06, 3.773466E+06, 4.494242E+06,
5.332883E+06, 6.305759E+06, 7.431058E+06, 8.728947E+06, 1.022174E+07, 1.193408E+07,
1.389313E+07, 1.612879E+07, 1.867388E+07, 2.156440E+07, 2.483975E+07, 2.854298E+07,
3.272105E+07, 3.742513E+07, 4.271087E+07, 4.863870E+07, 5.527418E+07, 6.268830E+07,
7.095785E+07, 8.016580E+07, 9.040167E+07, 1.017619E+08, 1.143503E+08, 1.282786E+08,
1.436666E+08, 1.606431E+08, 1.793457E+08, 1.999222E+08, 2.225305E+08, 2.473392E+08,
2.745284E+08, 3.042904E+08, 3.368300E+08, 3.723651E+08, 4.111276E+08, 4.533644E+08,
4.993372E+08, 5.493242E+08, 6.036203E+08, 6.625380E+08, 7.264084E+08, 7.955819E+08,
8.704289E+08, 9.513412E+08, 1.038732E+09, 1.133039E+09, 1.234722E+09, 1.344267E+09,
1.462186E+09, 1.589017E+09, 1.725329E+09, 1.871716E+09, 2.028808E+09, 2.197261E+09,
2.377770E+09, 2.571060E+09, 2.777894E+09, 2.999070E+09, 3.235427E+09, 3.487843E+09,
3.757235E+09, 4.044568E+09, 4.350846E+09, 4.677124E+09, 5.024499E+09, 5.394123E+09,
5.787198E+09, 6.204973E+09, 6.648762E+09, 7.119927E+09, 7.619892E+09, 8.150140E+09,
8.712220E+09, 9.307740E+09, 9.938379E+09, 1.060588E+10, 1.131207E+10, 1.205882E+10,
1.284811E+10, 1.368199E+10, 1.456256E+10, 1.549205E+10, 1.647274E+10, 1.750701E+10,
1.859732E+10, 1.974625E+10, 2.095645E+10, 2.223068E+10, 2.357178E+10, 2.498274E+10,
2.646661E+10, 2.802658E+10, 2.966593E+10, 3.138808E+10, 3.319655E+10, 3.509501E+10,
3.708723E+10, 3.917710E+10, 4.136869E+10, 4.366616E+10, 4.607384E+10, 4.859618E+10,
5.123780E+10, 5.400345E+10, 5.689808E+10, 5.992673E+10, 6.309465E+10, 6.640727E+10,
6.987015E+10, 7.348904E+10, 7.726990E+10, 8.121884E+10, 8.534217E+10, 8.964640E+10,
9.413822E+10, 9.882455E+10, 1.037125E+11, 1.088094E+11, 1.141229E+11, 1.196605E+11,
1.254305E+11, 1.314410E+11, 1.377004E+11, 1.442175E+11, 1.510013E+11, 1.580609E+11,
1.654059E+11, 1.730460E+11, 1.809912E+11, 1.892519E+11, 1.978386E+11, 2.067621E+11,
2.160338E+11, 2.256649E+11, 2.356674E+11, 2.460533E+11, 2.568351E+11, 2.680255E+11,
2.796376E+11, 2.916849E+11, 3.041811E+11, 3.171404E+11, 3.305773E+11, 3.445067E+11,
3.589439E+11, 3.739045E+11, 3.894045E+11, 4.054605E+11, 4.220892E+11, 4.393081E+11,
4.571347E+11, 4.755873E+11, 4.946844E+11, 5.144452E+11, 5.348889E+11, 5.560357E+11,
5.779061E+11, 6.005211E+11, 6.239019E+11, 6.480705E+11, 6.730496E+11, 6.988620E+11,
7.255312E+11, 7.530815E+11, 7.815372E+11, 8.109238E+11, 8.412671E+11, 8.725931E+11,
9.049290E+11, 9.383023E+11, 9.727413E+11, 1.008275E+12, 1.044932E+12, 1.082743E+12,
1.121739E+12, 1.161951E+12, 1.203411E+12, 1.246152E+12, 1.290208E+12, 1.335613E+12,
1.382402E+12, 1.430610E+12, 1.480274E+12, 1.531431E+12, 1.584120E+12, 1.638379E+12,
1.694247E+12, 1.751766E+12, 1.810976E+12, 1.871920E+12, 1.934640E+12, 1.999181E+12,
2.065587E+12, 2.133904E+12, 2.204179E+12, 2.276458E+12, 2.350791E+12, 2.427227E+12,
2.505817E+12, 2.586611E+12, 2.669663E+12, 2.755024E+12, 2.842752E+12, 2.932900E+12,
3.025525E+12, 3.120685E+12, 3.218439E+12, 3.318847E+12, 3.421969E+12, 3.527869E+12,
3.636609E+12, 3.748253E+12, 3.862869E+12, 3.980522E+12, 4.101281E+12, 4.225217E+12,
4.352398E+12, 4.482897E+12, 4.616789E+12, 4.754148E+12, 4.895049E+12, 5.039569E+12,
5.187790E+12, 5.339789E+12, 5.495649E+12, 5.655455E+12, 5.819289E+12, 5.987238E+12,
6.159389E+12, 6.335833E+12, 6.516660E+12, 6.701962E+12, 6.891832E+12,
])
# ---------------------- M = 50, I = 1 ---------------------------
M = 50
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.769600E+00, 4.269987E+01, 9.814561E+01, 1.552356E+02, 2.128150E+02, 2.706077E+02,
3.285146E+02, 3.864951E+02, 4.445326E+02, 5.026278E+02, 5.608132E+02, 6.191261E+02,
6.776212E+02, 7.363736E+02, 7.954647E+02, 8.549797E+02, 9.149952E+02, 9.755928E+02,
1.036859E+03, 1.098863E+03, 1.161685E+03, 1.225362E+03, 1.289972E+03, 1.355557E+03,
1.422167E+03, 1.489851E+03, 1.558626E+03, 1.628532E+03, 1.699607E+03, 1.771871E+03,
1.845355E+03, 1.920070E+03, 1.996043E+03, 2.073277E+03, 2.151791E+03, 2.231603E+03,
2.312747E+03, 2.395195E+03, 2.478975E+03, 2.564097E+03, 2.650564E+03, 2.738380E+03,
2.827570E+03, 2.918108E+03, 3.010041E+03, 3.103340E+03, 3.198024E+03, 3.294087E+03,
3.391545E+03, 3.490387E+03, 3.590631E+03, 3.692292E+03, 3.795325E+03, 3.899805E+03,
4.005654E+03, 4.112914E+03, 4.221598E+03, 4.331719E+03, 4.443221E+03, 4.556148E+03,
4.670476E+03, 4.786249E+03, 4.903443E+03, 5.022065E+03, 5.142089E+03, 5.263558E+03,
5.386445E+03, 5.510755E+03, 5.636496E+03, 5.763636E+03, 5.892261E+03, 6.022257E+03,
6.153753E+03, 6.286630E+03, 6.420936E+03, 6.556719E+03, 6.693898E+03, 6.832523E+03,
6.972598E+03, 7.114083E+03, 7.257028E+03, 7.401392E+03, 7.547226E+03, 7.694488E+03,
7.843181E+03, 7.993308E+03, 8.144875E+03, 8.297883E+03, 8.452338E+03, 8.608242E+03,
8.765548E+03, 8.924362E+03, 9.084530E+03, 9.246215E+03, 9.409312E+03, 9.573879E+03,
9.739863E+03, 9.907323E+03, 1.007621E+04, 1.024651E+04, 1.041830E+04, 1.059152E+04,
1.076618E+04, 1.094226E+04, 1.111984E+04, 1.129880E+04, 1.147925E+04, 1.166115E+04,
1.184449E+04, 1.202927E+04, 1.221551E+04, 1.240319E+04, 1.259232E+04, 1.278283E+04,
1.297487E+04, 1.316836E+04, 1.336323E+04, 1.355957E+04, 1.375736E+04, 1.395662E+04,
1.415733E+04, 1.435944E+04, 1.456301E+04, 1.476805E+04, 1.497455E+04, 1.518245E+04,
1.539182E+04, 1.560258E+04, 1.581489E+04, 1.602852E+04, 1.624369E+04, 1.646026E+04,
1.667823E+04, 1.689775E+04, 1.711859E+04, 1.734099E+04, 1.756470E+04, 1.778997E+04,
1.801664E+04, 1.824470E+04, 1.847424E+04, 1.870518E+04, 1.893760E+04, 1.917141E+04,
1.940670E+04, 1.964339E+04, 1.988156E+04, 2.012112E+04, 2.036208E+04, 2.060452E+04,
2.084835E+04, 2.109366E+04, 2.134037E+04, 2.158846E+04, 2.183804E+04, 2.208901E+04,
2.234136E+04, 2.259519E+04, 2.285042E+04, 2.310712E+04, 2.336520E+04, 2.362467E+04,
2.388553E+04, 2.414786E+04, 2.441157E+04, 2.467677E+04, 2.494324E+04, 2.521119E+04,
2.548061E+04, 2.575131E+04, 2.602348E+04, 2.629703E+04, 2.657195E+04, 2.684834E+04,
2.712610E+04, 2.740522E+04,
])
# ---------------------- M = 50, I = 2 ---------------------------
M = 50
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.784190E+00, 4.344159E+01, 9.997170E+01, 1.581917E+02, 2.169147E+02, 2.758572E+02,
3.349170E+02, 3.940525E+02, 4.532464E+02, 5.125045E+02, 5.718560E+02, 6.313451E+02,
6.910295E+02, 7.509947E+02, 8.113115E+02, 8.720752E+02, 9.333752E+02, 9.952881E+02,
1.057893E+03, 1.121284E+03, 1.185511E+03, 1.250646E+03, 1.316747E+03, 1.383869E+03,
1.452039E+03, 1.521334E+03, 1.591771E+03, 1.663379E+03, 1.736195E+03, 1.810241E+03,
1.885547E+03, 1.962127E+03, 2.039987E+03, 2.119187E+03, 2.199692E+03, 2.281556E+03,
2.364777E+03, 2.449343E+03, 2.535286E+03, 2.622613E+03, 2.711330E+03, 2.801463E+03,
2.892990E+03, 2.985909E+03, 3.080268E+03, 3.176036E+03, 3.273235E+03, 3.371855E+03,
3.471916E+03, 3.573406E+03, 3.676342E+03, 3.780709E+03, 3.886523E+03, 3.993797E+03,
4.102516E+03, 4.212690E+03, 4.324334E+03, 4.437392E+03, 4.551942E+03, 4.667963E+03,
4.785429E+03, 4.904350E+03, 5.024735E+03, 5.146593E+03, 5.269896E+03, 5.394689E+03,
5.520982E+03, 5.648704E+03, 5.777902E+03, 5.908582E+03, 6.040712E+03, 6.174338E+03,
6.309425E+03, 6.446022E+03, 6.584049E+03, 6.723599E+03, 6.864589E+03, 7.007068E+03,
7.151042E+03, 7.296470E+03, 7.443403E+03, 7.591798E+03, 7.741660E+03, 7.893042E+03,
8.045899E+03, 8.200235E+03, 8.356054E+03, 8.513307E+03, 8.672103E+03, 8.832393E+03,
8.994127E+03, 9.157362E+03, 9.322101E+03, 9.488294E+03, 9.655997E+03, 9.825158E+03,
9.995836E+03, 1.016798E+04, 1.034164E+04, 1.051677E+04, 1.069338E+04, 1.087151E+04,
1.105106E+04, 1.123214E+04, 1.141471E+04, 1.159882E+04, 1.178434E+04, 1.197142E+04,
1.215991E+04, 1.234990E+04, 1.254144E+04, 1.273440E+04, 1.292893E+04, 1.312488E+04,
1.332233E+04, 1.352128E+04, 1.372172E+04, 1.392367E+04, 1.412705E+04, 1.433200E+04,
1.453839E+04, 1.474621E+04, 1.495561E+04, 1.516644E+04, 1.537878E+04, 1.559263E+04,
1.580792E+04, 1.602472E+04, 1.624304E+04, 1.646279E+04, 1.668405E+04, 1.690675E+04,
1.713097E+04, 1.735671E+04, 1.758388E+04, 1.781249E+04, 1.804261E+04, 1.827426E+04,
1.850734E+04, 1.874195E+04, 1.897798E+04, 1.921554E+04, 1.945454E+04, 1.969497E+04,
1.993692E+04, 2.018039E+04, 2.042530E+04, 2.067164E+04, 2.091950E+04, 2.116879E+04,
2.141952E+04, 2.167176E+04, 2.192553E+04, 2.218063E+04, 2.243725E+04, 2.269540E+04,
2.295488E+04, 2.321596E+04, 2.347839E+04, 2.374232E+04, 2.400769E+04, 2.427447E+04,
2.454277E+04, 2.481249E+04, 2.508362E+04, 2.535627E+04, 2.563034E+04, 2.590582E+04,
2.618281E+04, 2.646111E+04, 2.674103E+04, 2.702225E+04, 2.730487E+04, 2.758901E+04,
2.787455E+04, 2.816159E+04,
])
# ---------------------- M = 50, I = 3 ---------------------------
M = 50
I = 3
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
1.829360E+00, 4.567662E+01, 1.054716E+02, 1.670941E+02, 2.292611E+02, 2.916655E+02,
3.541968E+02, 4.168097E+02, 4.794904E+02, 5.422517E+02, 6.051222E+02, 6.681570E+02,
7.314428E+02, 7.950479E+02, 8.590830E+02, 9.236403E+02, 9.888198E+02, 1.054709E+03,
1.121396E+03, 1.188975E+03, 1.257504E+03, 1.327060E+03, 1.397697E+03, 1.469475E+03,
1.542453E+03, 1.616657E+03, 1.692120E+03, 1.768904E+03, 1.847020E+03, 1.926507E+03,
2.007363E+03, 2.089638E+03, 2.173342E+03, 2.258478E+03, 2.345089E+03, 2.433172E+03,
2.522743E+03, 2.613793E+03, 2.706354E+03, 2.800433E+03, 2.896036E+03, 2.993166E+03,
3.091851E+03, 3.192062E+03, 3.293823E+03, 3.397129E+03, 3.502001E+03, 3.608430E+03,
3.716436E+03, 3.826007E+03, 3.937129E+03, 4.049850E+03, 4.164154E+03, 4.280024E+03,
4.397473E+03, 4.516514E+03, 4.637127E+03, 4.759358E+03, 4.883149E+03, 5.008545E+03,
5.135559E+03, 5.264164E+03, 5.394332E+03, 5.526108E+03, 5.659504E+03, 5.794488E+03,
5.931067E+03, 6.069251E+03, 6.209046E+03, 6.350461E+03, 6.493459E+03, 6.638047E+03,
6.784276E+03, 6.932107E+03, 7.081546E+03, 7.232601E+03, 7.385228E+03, 7.539480E+03,
7.695363E+03, 7.852884E+03, 8.011947E+03, 8.172707E+03, 8.335018E+03, 8.498934E+03,
8.664511E+03, 8.831702E+03, 9.000458E+03, 9.170889E+03, 9.342891E+03, 9.516521E+03,
9.691784E+03, 9.868684E+03, 1.004717E+04, 1.022729E+04, 1.040901E+04, 1.059237E+04,
1.077733E+04, 1.096388E+04, 1.115209E+04, 1.134190E+04, 1.153338E+04, 1.172646E+04,
1.192108E+04, 1.211738E+04, 1.231535E+04, 1.251487E+04, 1.271601E+04, 1.291883E+04,
1.312320E+04, 1.332925E+04, 1.353687E+04, 1.374610E+04, 1.395703E+04, 1.416951E+04,
1.438362E+04, 1.459936E+04, 1.481666E+04, 1.503566E+04, 1.525623E+04, 1.547842E+04,
1.570225E+04, 1.592764E+04, 1.615467E+04, 1.638333E+04, 1.661363E+04, 1.684549E+04,
1.707899E+04, 1.731414E+04, 1.755084E+04, 1.778910E+04, 1.802909E+04, 1.827064E+04,
1.851375E+04, 1.875850E+04, 1.900489E+04, 1.925285E+04, 1.950236E+04, 1.975360E+04,
2.000631E+04, 2.026067E+04, 2.051667E+04, 2.077423E+04, 2.103334E+04, 2.129409E+04,
2.155649E+04, 2.182035E+04, 2.208594E+04, 2.235299E+04, 2.262168E+04, 2.289202E+04,
2.316380E+04, 2.343732E+04, 2.371229E+04, 2.398890E+04, 2.426705E+04, 2.454684E+04,
2.482816E+04, 2.511102E+04, 2.539552E+04, 2.568155E+04, 2.596912E+04, 2.625831E+04,
2.654893E+04, 2.684129E+04, 2.713506E+04, 2.743046E+04, 2.772739E+04, 2.802583E+04,
2.832590E+04, 2.862748E+04, 2.893058E+04, 2.923518E+04, 2.954141E+04, 2.984914E+04,
3.015838E+04, 3.046922E+04,
])
# ---------------------- M = 51, I = 1 ---------------------------
M = 51
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
2.242436E+01, 7.725486E+02, 2.121419E+03, 3.890178E+03, 6.013062E+03, 8.504252E+03,
1.142366E+04, 1.485658E+04, 1.890626E+04, 2.369326E+04, 2.935867E+04, 3.606709E+04,
4.401067E+04, 5.341521E+04, 6.454346E+04, 7.771136E+04, 9.328406E+04, 1.116825E+05,
1.334005E+05, 1.590135E+05, 1.891903E+05, 2.247090E+05, 2.664698E+05, 3.155170E+05,
3.730570E+05, 4.404840E+05, 5.194095E+05, 6.116882E+05, 7.194554E+05, 8.451682E+05,
9.916491E+05, 1.162137E+06, 1.360343E+06, 1.590521E+06, 1.857532E+06, 2.166934E+06,
2.525070E+06, 2.939175E+06, 3.417490E+06, 3.969394E+06, 4.605551E+06, 5.338073E+06,
6.180709E+06, 7.149044E+06, 8.260724E+06, 9.535725E+06, 1.099663E+07, 1.266894E+07,
1.458144E+07, 1.676658E+07, 1.926092E+07, 2.210560E+07, 2.534690E+07, 2.903680E+07,
3.323367E+07, 3.800297E+07, 4.341808E+07, 4.956117E+07, 5.652419E+07, 6.440992E+07,
7.333322E+07, 8.342230E+07, 9.482017E+07, 1.076862E+08, 1.221980E+08, 1.385530E+08,
1.569711E+08, 1.776964E+08, 2.010001E+08, 2.271832E+08, 2.565793E+08, 2.895582E+08,
3.265296E+08, 3.679465E+08, 4.143103E+08, 4.661747E+08, 5.241516E+08, 5.889162E+08,
6.612133E+08, 7.418637E+08, 8.317719E+08, 9.319336E+08, 1.043444E+09, 1.167508E+09,
1.305450E+09, 1.458722E+09, 1.628922E+09, 1.817798E+09, 2.027272E+09, 2.259444E+09,
2.516619E+09, 2.801317E+09, 3.116294E+09, 3.464566E+09, 3.849427E+09, 4.274473E+09,
4.743631E+09, 5.261186E+09, 5.831808E+09, 6.460589E+09, 7.153075E+09, 7.915304E+09,
8.753845E+09, 9.675848E+09, 1.068908E+10, 1.180199E+10, 1.302374E+10, 1.436429E+10,
1.583444E+10, 1.744592E+10, 1.921141E+10, 2.114470E+10, 2.326068E+10, 2.557551E+10,
2.810664E+10, 3.087298E+10, 3.389497E+10, 3.719468E+10, 4.079597E+10, 4.472462E+10,
4.900843E+10, 5.367742E+10, 5.876395E+10, 6.430294E+10, 7.033198E+10, 7.689160E+10,
8.402544E+10, 9.178048E+10, 1.002073E+11, 1.093602E+11, 1.192976E+11, 1.300825E+11,
1.417824E+11, 1.544698E+11, 1.682225E+11, 1.831241E+11, 1.992643E+11, 2.167394E+11,
2.356524E+11, 2.561139E+11, 2.782424E+11, 3.021646E+11, 3.280164E+11, 3.559434E+11,
3.861009E+11, 4.186556E+11, 4.537853E+11, 4.916803E+11, 5.325440E+11, 5.765937E+11,
6.240612E+11, 6.751945E+11, 7.302577E+11, 7.895332E+11, 8.533219E+11, 9.219446E+11,
9.957437E+11, 1.075084E+12, 1.160353E+12, 1.251966E+12, 1.350364E+12, 1.456014E+12,
1.569417E+12, 1.691105E+12, 1.821642E+12, 1.961629E+12, 2.111705E+12, 2.272549E+12,
2.444884E+12, 2.629474E+12, 2.827135E+12, 3.038731E+12, 3.265180E+12, 3.507453E+12,
3.766586E+12, 4.043672E+12,
])
# ---------------------- M = 52, I = 1 ---------------------------
M = 52
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
2.000000E-06, 9.439835E+00, 2.871728E+01, 5.406319E+01, 8.449555E+01, 1.193488E+02,
1.582205E+02, 2.008949E+02, 2.472961E+02, 2.974472E+02, 3.514358E+02, 4.093897E+02,
4.714617E+02, 5.378208E+02, 6.086474E+02, 6.841307E+02, 7.644677E+02, 8.498620E+02,
9.405242E+02, 1.036671E+03, 1.138525E+03, 1.246317E+03, 1.360281E+03, 1.480661E+03,
1.607706E+03, 1.741671E+03, 1.882822E+03, 2.031428E+03, 2.187770E+03, 2.352137E+03,
2.524823E+03, 2.706136E+03, 2.896391E+03, 3.095913E+03, 3.305037E+03, 3.524108E+03,
3.753482E+03, 3.993526E+03, 4.244617E+03, 4.507146E+03, 4.781511E+03, 5.068125E+03,
5.367413E+03, 5.679810E+03, 6.005763E+03, 6.345735E+03, 6.700197E+03, 7.069636E+03,
7.454550E+03, 7.855452E+03, 8.272866E+03, 8.707330E+03, 9.159397E+03, 9.629633E+03,
1.011862E+04, 1.062694E+04, 1.115522E+04, 1.170407E+04, 1.227414E+04, 1.286606E+04,
1.348052E+04, 1.411819E+04, 1.477977E+04, 1.546598E+04, 1.617755E+04, 1.691522E+04,
1.767975E+04, 1.847193E+04, 1.929255E+04, 2.014242E+04, 2.102237E+04, 2.193326E+04,
2.287594E+04, 2.385130E+04, 2.486024E+04, 2.590368E+04, 2.698256E+04, 2.809783E+04,
2.925048E+04, 3.044150E+04, 3.167190E+04, 3.294272E+04, 3.425501E+04, 3.560985E+04,
3.700835E+04, 3.845160E+04, 3.994076E+04, 4.147698E+04, 4.306145E+04, 4.469536E+04,
4.637995E+04, 4.811646E+04, 4.990616E+04, 5.175035E+04, 5.365034E+04, 5.560747E+04,
5.762312E+04, 5.969865E+04, 6.183550E+04, 6.403509E+04, 6.629888E+04, 6.862837E+04,
7.102507E+04, 7.349051E+04, 7.602625E+04, 7.863389E+04, 8.131504E+04, 8.407135E+04,
8.690448E+04, 8.981613E+04, 9.280803E+04, 9.588192E+04, 9.903959E+04, 1.022828E+05,
1.056135E+05, 1.090335E+05, 1.125446E+05, 1.161488E+05, 1.198481E+05, 1.236444E+05,
1.275397E+05, 1.315362E+05, 1.356358E+05, 1.398406E+05, 1.441529E+05, 1.485747E+05,
1.531082E+05, 1.577558E+05, 1.625195E+05, 1.674018E+05, 1.724050E+05, 1.775314E+05,
1.827833E+05, 1.881633E+05, 1.936738E+05, 1.993172E+05, 2.050961E+05, 2.110130E+05,
2.170706E+05, 2.232714E+05, 2.296181E+05, 2.361134E+05, 2.427600E+05, 2.495607E+05,
2.565183E+05, 2.636356E+05, 2.709155E+05, 2.783609E+05, 2.859747E+05, 2.937599E+05,
3.017195E+05, 3.098566E+05, 3.181742E+05, 3.266755E+05, 3.353636E+05, 3.442417E+05,
3.533131E+05, 3.625811E+05, 3.720488E+05, 3.817198E+05, 3.915973E+05, 4.016848E+05,
4.119858E+05, 4.225038E+05, 4.332422E+05, 4.442047E+05, 4.553950E+05, 4.668165E+05,
4.784731E+05, 4.903685E+05, 5.025065E+05, 5.148908E+05, 5.275254E+05, 5.404141E+05,
5.535608E+05, 5.669696E+05,
])
# ---------------------- M = 53, I = 1 ---------------------------
M = 53
I = 1
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
3.357230E+00, 6.388395E+01, 1.276299E+02, 1.914331E+02, 2.556212E+02, 3.212115E+02,
3.897614E+02, 4.629667E+02, 5.424324E+02, 6.296189E+02, 7.258625E+02, 8.324166E+02,
9.504891E+02, 1.081274E+03, 1.225973E+03, 1.385815E+03, 1.562070E+03, 1.756055E+03,
1.969145E+03, 2.202779E+03, 2.458461E+03, 2.737768E+03, 3.042351E+03, 3.373938E+03,
3.734336E+03, 4.125436E+03, 4.549211E+03, 5.007721E+03, 5.503115E+03, 6.037631E+03,
6.613599E+03, 7.233444E+03, 7.899685E+03, 8.614941E+03, 9.381928E+03, 1.020347E+04,
1.108248E+04, 1.202199E+04, 1.302514E+04, 1.409517E+04, 1.523543E+04, 1.644939E+04,
1.774064E+04, 1.911288E+04, 2.056992E+04, 2.211570E+04, 2.375428E+04, 2.548986E+04,
2.732674E+04, 2.926936E+04, 3.132230E+04, 3.349026E+04, 3.577808E+04, 3.819073E+04,
4.073333E+04, 4.341113E+04, 4.622952E+04, 4.919405E+04, 5.231040E+04, 5.558441E+04,
5.902205E+04, 6.262946E+04, 6.641295E+04, 7.037894E+04, 7.453405E+04, 7.888504E+04,
8.343883E+04, 8.820253E+04, 9.318338E+04, 9.838881E+04, 1.038264E+05, 1.095040E+05,
1.154294E+05, 1.216108E+05, 1.280566E+05, 1.347751E+05, 1.417750E+05, 1.490652E+05,
1.566547E+05, 1.645527E+05, 1.727687E+05, 1.813122E+05, 1.901930E+05, 1.994211E+05,
2.090067E+05, 2.189602E+05, 2.292922E+05, 2.400135E+05, 2.511350E+05, 2.626681E+05,
2.746241E+05, 2.870146E+05, 2.998515E+05, 3.131469E+05, 3.269131E+05, 3.411625E+05,
3.559080E+05, 3.711624E+05, 3.869389E+05, 4.032510E+05, 4.201123E+05, 4.375367E+05,
4.555383E+05, 4.741314E+05, 4.933307E+05, 5.131510E+05, 5.336073E+05, 5.547150E+05,
5.764896E+05, 5.989470E+05, 6.221033E+05, 6.459748E+05, 6.705781E+05, 6.959299E+05,
7.220475E+05, 7.489482E+05, 7.766496E+05, 8.051697E+05, 8.345266E+05, 8.647387E+05,
8.958248E+05, 9.278039E+05, 9.606952E+05, 9.945184E+05, 1.029293E+06, 1.065040E+06,
1.101778E+06, 1.139530E+06, 1.178315E+06, 1.218156E+06, 1.259073E+06, 1.301088E+06,
1.344225E+06, 1.388504E+06, 1.433950E+06, 1.480585E+06, 1.528432E+06, 1.577515E+06,
1.627859E+06, 1.679487E+06, 1.732424E+06, 1.786695E+06, 1.842326E+06, 1.899341E+06,
1.957768E+06, 2.017632E+06, 2.078959E+06, 2.141778E+06, 2.206114E+06, 2.271996E+06,
2.339451E+06, 2.408509E+06, 2.479196E+06, 2.551543E+06, 2.625579E+06, 2.701333E+06,
2.778836E+06, 2.858117E+06, 2.939208E+06, 3.022139E+06, 3.106942E+06, 3.193650E+06,
3.282293E+06, 3.372905E+06, 3.465518E+06, 3.560166E+06, 3.656882E+06, 3.755701E+06,
3.856657E+06, 3.959784E+06, 4.065119E+06, 4.172696E+06, 4.282551E+06, 4.394721E+06,
4.509242E+06, 4.626152E+06,
])
# ---------------------- M = 53, I = 2 ---------------------------
M = 53
I = 2
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
6.908470E+00, 1.316557E+02, 2.630372E+02, 3.945385E+02, 5.268460E+02, 6.620835E+02,
8.034970E+02, 9.546200E+02, 1.118799E+03, 1.299078E+03, 1.498248E+03, 1.718924E+03,
1.963631E+03, 2.234865E+03, 2.535144E+03, 2.867044E+03, 3.233225E+03, 3.636454E+03,
4.079620E+03, 4.565744E+03, 5.097990E+03, 5.679674E+03, 6.314270E+03, 7.005414E+03,
7.756911E+03, 8.572742E+03, 9.457061E+03, 1.041421E+04, 1.144871E+04, 1.256529E+04,
1.376884E+04, 1.506449E+04, 1.645754E+04, 1.795352E+04, 1.955815E+04, 2.127738E+04,
2.311737E+04, 2.508451E+04, 2.718541E+04, 2.942693E+04, 3.181613E+04, 3.436034E+04,
3.706712E+04, 3.994428E+04, 4.299988E+04, 4.624224E+04, 4.967993E+04, 5.332180E+04,
5.717695E+04, 6.125477E+04, 6.556491E+04, 7.011731E+04, 7.492218E+04, 7.999005E+04,
8.533172E+04, 9.095828E+04, 9.688115E+04, 1.031120E+05, 1.096629E+05, 1.165462E+05,
1.237744E+05, 1.313607E+05, 1.393182E+05, 1.476606E+05, 1.564019E+05, 1.655564E+05,
1.751387E+05, 1.851638E+05, 1.956471E+05, 2.066043E+05, 2.180514E+05, 2.300049E+05,
2.424815E+05, 2.554985E+05, 2.690734E+05, 2.832241E+05, 2.979690E+05, 3.133267E+05,
3.293164E+05, 3.459575E+05, 3.632700E+05, 3.812742E+05, 3.999909E+05, 4.194410E+05,
4.396463E+05, 4.606288E+05, 4.824107E+05, 5.050151E+05, 5.284651E+05, 5.527846E+05,
5.779977E+05, 6.041290E+05, 6.312037E+05, 6.592472E+05, 6.882857E+05, 7.183455E+05,
7.494537E+05, 7.816377E+05, 8.149253E+05, 8.493451E+05, 8.849259E+05, 9.216971E+05,
9.596886E+05, 9.989308E+05, 1.039455E+06, 1.081291E+06, 1.124473E+06, 1.169032E+06,
1.215002E+06, 1.262415E+06, 1.311307E+06, 1.361711E+06, 1.413663E+06, 1.467198E+06,
1.522353E+06, 1.579164E+06, 1.637669E+06, 1.697906E+06, 1.759913E+06, 1.823730E+06,
1.889395E+06, 1.956950E+06, 2.026435E+06, 2.097891E+06, 2.171361E+06, 2.246887E+06,
2.324512E+06, 2.404281E+06, 2.486237E+06, 2.570426E+06, 2.656894E+06, 2.745687E+06,
2.836852E+06, 2.930436E+06, 3.026488E+06, 3.125057E+06, 3.226192E+06, 3.329943E+06,
3.436362E+06, 3.545501E+06, 3.657410E+06, 3.772143E+06, 3.889755E+06, 4.010298E+06,
4.133829E+06, 4.260402E+06, 4.390074E+06, 4.522903E+06, 4.658945E+06, 4.798260E+06,
4.940907E+06, 5.086945E+06, 5.236436E+06, 5.389440E+06, 5.546021E+06, 5.706240E+06,
5.870161E+06, 6.037850E+06, 6.209371E+06, 6.384789E+06, 6.564172E+06, 6.747587E+06,
6.935102E+06, 7.126787E+06, 7.322711E+06, 7.522944E+06, 7.727559E+06, 7.936627E+06,
8.150221E+06, 8.368415E+06, 8.591283E+06, 8.818902E+06, 9.051347E+06, 9.288696E+06,
9.531026E+06, 9.778416E+06,
])
# ---------------------- M = 53, I = 3 ---------------------------
M = 53
I = 3
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
2.724759E+01, 5.188821E+02, 1.036664E+03, 1.554914E+03, 2.076315E+03, 2.609185E+03,
3.166242E+03, 3.761337E+03, 4.407580E+03, 5.116904E+03, 5.900230E+03, 6.767805E+03,
7.729507E+03, 8.795104E+03, 9.974440E+03, 1.127758E+04, 1.271492E+04, 1.429726E+04,
1.603589E+04, 1.794258E+04, 2.002969E+04, 2.231015E+04, 2.479752E+04, 2.750597E+04,
3.045034E+04, 3.364615E+04, 3.710960E+04, 4.085761E+04, 4.490782E+04, 4.927862E+04,
5.398913E+04, 5.905927E+04, 6.450975E+04, 7.036207E+04, 7.663855E+04, 8.336236E+04,
9.055751E+04, 9.824887E+04, 1.064622E+05, 1.152242E+05, 1.245624E+05, 1.345053E+05,
1.450823E+05, 1.563239E+05, 1.682615E+05, 1.809275E+05, 1.943551E+05, 2.085790E+05,
2.236344E+05, 2.395580E+05, 2.563872E+05, 2.741609E+05, 2.929188E+05, 3.127018E+05,
3.335519E+05, 3.555124E+05, 3.786276E+05, 4.029431E+05, 4.285056E+05, 4.553632E+05,
4.835651E+05, 5.131617E+05, 5.442048E+05, 5.767474E+05, 6.108439E+05, 6.465498E+05,
6.839223E+05, 7.230196E+05, 7.639014E+05, 8.066289E+05, 8.512645E+05, 8.978721E+05,
9.465171E+05, 9.972663E+05, 1.050188E+06, 1.105352E+06, 1.162829E+06, 1.222692E+06,
1.285016E+06, 1.349877E+06, 1.417351E+06, 1.487518E+06, 1.560458E+06, 1.636254E+06,
1.714990E+06, 1.796750E+06, 1.881623E+06, 1.969697E+06, 2.061062E+06, 2.155812E+06,
2.254039E+06, 2.355840E+06, 2.461313E+06, 2.570556E+06, 2.683671E+06, 2.800761E+06,
2.921930E+06, 3.047286E+06, 3.176937E+06, 3.310992E+06, 3.449566E+06, 3.592771E+06,
3.740724E+06, 3.893544E+06, 4.051350E+06, 4.214264E+06, 4.382412E+06, 4.555918E+06,
4.734911E+06, 4.919521E+06, 5.109882E+06, 5.306126E+06, 5.508392E+06, 5.716817E+06,
5.931542E+06, 6.152711E+06, 6.380469E+06, 6.614963E+06, 6.856342E+06, 7.104760E+06,
7.360369E+06, 7.623326E+06, 7.893790E+06, 8.171922E+06, 8.457886E+06, 8.751846E+06,
9.053972E+06, 9.364433E+06, 9.683403E+06, 1.001106E+07, 1.034757E+07, 1.069313E+07,
1.104791E+07, 1.141209E+07, 1.178588E+07, 1.216945E+07, 1.256300E+07, 1.296673E+07,
1.338083E+07, 1.380550E+07, 1.424095E+07, 1.468738E+07, 1.514500E+07, 1.561402E+07,
1.609465E+07, 1.658712E+07, 1.709163E+07, 1.760842E+07, 1.813770E+07, 1.867971E+07,
1.923467E+07, 1.980281E+07, 2.038438E+07, 2.097961E+07, 2.158874E+07, 2.221202E+07,
2.284969E+07, 2.350201E+07, 2.416922E+07, 2.485159E+07, 2.554937E+07, 2.626282E+07,
2.699222E+07, 2.773782E+07, 2.849990E+07, 2.927873E+07, 3.007460E+07, 3.088777E+07,
3.171854E+07, 3.256719E+07, 3.343401E+07, 3.431930E+07, 3.522335E+07, 3.614645E+07,
3.708892E+07, 3.805106E+07,
])
# ---------------------- M = 53, I = 4 ---------------------------
M = 53
I = 4
TIPS_2017_ISOT_HASH[(M,I)] = TIPS_2017_ISOT[2]
TIPS_2017_ISOQ_HASH[(M,I)] = float64([
6.713360E+00, 1.277459E+02, 2.552158E+02, 3.828120E+02, 5.112785E+02, 6.428265E+02,
7.807297E+02, 9.284754E+02, 1.089325E+03, 1.266231E+03, 1.461891E+03, 1.678831E+03,
1.919481E+03, 2.186220E+03, 2.481426E+03, 2.807498E+03, 3.166878E+03, 3.562071E+03,
3.995649E+03, 4.470269E+03, 4.988676E+03, 5.553712E+03, 6.168321E+03, 6.835557E+03,
7.558585E+03, 8.340692E+03, 9.185283E+03, 1.009589E+04, 1.107619E+04, 1.212996E+04,
1.326116E+04, 1.447384E+04, 1.577225E+04, 1.716074E+04, 1.864383E+04, 2.022620E+04,
2.191268E+04, 2.370826E+04, 2.561808E+04, 2.764746E+04, 2.980188E+04, 3.208700E+04,
3.450865E+04, 3.707282E+04, 3.978569E+04, 4.265362E+04, 4.568317E+04, 4.888106E+04,
5.225421E+04, 5.580974E+04, 5.955495E+04, 6.349734E+04, 6.764462E+04, 7.200469E+04,
7.658567E+04, 8.139588E+04, 8.644384E+04, 9.173829E+04, 9.728821E+04, 1.031028E+05,
1.091913E+05, 1.155636E+05, 1.222293E+05, 1.291987E+05, 1.364819E+05, 1.440896E+05,
1.520325E+05, 1.603216E+05, 1.689683E+05, 1.779840E+05, 1.873805E+05, 1.971699E+05,
2.073643E+05, 2.179763E+05, 2.290187E+05, 2.405046E+05, 2.524472E+05, 2.648602E+05,
2.777574E+05, 2.911528E+05, 3.050611E+05, 3.194967E+05, 3.344747E+05, 3.500103E+05,
3.661191E+05, 3.828169E+05, 4.001198E+05, 4.180442E+05, 4.366069E+05, 4.558248E+05,
4.757153E+05, 4.962961E+05, 5.175850E+05, 5.396004E+05, 5.623607E+05, 5.858850E+05,
6.101923E+05, 6.353023E+05, 6.612348E+05, 6.880100E+05, 7.156484E+05, 7.441709E+05,
7.735987E+05, 8.039534E+05, 8.352568E+05, 8.675311E+05, 9.007991E+05, 9.350835E+05,
9.704078E+05, 1.006796E+06, 1.044271E+06, 1.082858E+06, 1.122582E+06, 1.163467E+06,
1.205540E+06, 1.248826E+06, 1.293351E+06, 1.339142E+06, 1.386227E+06, 1.434632E+06,
1.484385E+06, 1.535514E+06, 1.588049E+06, 1.642018E+06, 1.697450E+06, 1.754375E+06,
1.812824E+06, 1.872828E+06, 1.934416E+06, 1.997621E+06, 2.062474E+06, 2.129008E+06,
2.197256E+06, 2.267251E+06, 2.339026E+06, 2.412615E+06, 2.488053E+06, 2.565375E+06,
2.644616E+06, 2.725811E+06, 2.808998E+06, 2.894213E+06, 2.981493E+06, 3.070876E+06,
3.162399E+06, 3.256102E+06, 3.352023E+06, 3.450202E+06, 3.550679E+06, 3.653494E+06,
3.758688E+06, 3.866303E+06, 3.976381E+06, 4.088964E+06, 4.204094E+06, 4.321816E+06,
4.442173E+06, 4.565209E+06, 4.690970E+06, 4.819501E+06, 4.950847E+06, 5.085056E+06,
5.222174E+06, 5.362248E+06, 5.505328E+06, 5.651460E+06, 5.800694E+06, 5.953081E+06,
6.108669E+06, 6.267510E+06, 6.429655E+06, 6.595155E+06, 6.764063E+06, 6.936432E+06,
7.112315E+06, 7.291766E+06,
])
| [
"prajwalniraula@gmail.com"
] | prajwalniraula@gmail.com |
2c4dc8c8b16b27465242d2d8d835d84ebf29c7e8 | 417b69d242a6b1e388b3715172d49cc6fff72742 | /basic/python-oops/test-constructor.py | 82cc0dd559ebf3d0312b2f75534d1f3840e460fa | [] | no_license | NaveenSingh4u/python-learning | a3626fee3a19fa7a3fca2bd9678cf120afe18771 | 86fbfee396910dea7524045d17e90b28b6f7eef1 | refs/heads/master | 2020-12-21T06:11:36.931034 | 2020-02-16T14:48:57 | 2020-02-16T14:48:57 | 236,333,751 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 289 | py | class Test:
count = 0
def __init__(self):
Test.count = Test.count + 1
@classmethod
def getNoOfobject(cls):
print('The number of object created', cls.count)
t1 = Test()
t2 = Test()
t3 = Test()
t3.getNoOfobject()
t4 = Test()
t5 = Test()
t5.getNoOfobject() | [
"singh.naveen0004@gmail.com"
] | singh.naveen0004@gmail.com |
d68d032e544410088da51547b06a4ca3e587f2b2 | f78e7917536b5ce8630fcab47428254fe6814591 | /RIK_simulator/src/lbd_playback/bin/playbackUtils.py | 12677392f8e812bce5f673014702e0941273b105 | [] | no_license | xuezhizeng/hwang_robot_works | da507993dbfb278e6981304d988d44e263a8b981 | 211f6aede9e5929ca4d174e8d1c28b3b3082752c | refs/heads/master | 2020-03-07T14:31:44.041890 | 2017-09-05T15:25:23 | 2017-09-05T15:25:23 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,276 | py | import numpy as np
import IK.transformations as T
from usingRosBag_linear import *
from IK.tongsCenter import *
class PlaybackUtils:
def __init__(self, vars):
# Global variables
self.positionDisplacement = np.array([0,0,-0.22])
self.vars = vars
# self.positionDisplacement = np.array([0,0,-0.38])
self.tongsLength = 0.2286
self.gripperLength = 0.1524
def getNextDataColumn(self, time, parser, tongsTransform):
'''
:param time: the desired time to be found (in seconds)
:param parser: the bag parser that contains the data table
:param tongsTransform: tongs transform object to get center
:return: pos, quaternion, encoder, force values corresponding to the given time
'''
timeIdx = self.find_closest(parser.timeStampArray, time)
time = parser.resample_time_stamp[timeIdx]
pos = parser.vivePos_interpolated[timeIdx]
pos = self.transformPosition(pos)
quat = parser.viveQuat_interpolated[timeIdx]
encoder = parser.encoderarray_interpolated[timeIdx]
pos = tongsTransform.getCenterPosition(pos,quat,encoder)
return time, pos, quat, encoder
def find_closest(self,A, target):
#A must be sorted
idx = A.searchsorted(target)
idx = np.clip(idx, 1, len(A)-1)
left = A[idx-1]
right = A[idx]
idx -= target - left < right - target
return idx
def checkValidConfig(self,pos, quat):
'''
checks if the given position and orientation are reachable by the ur5 robot arm
:param pos:
:param quat:
:return:
'''
# stub
return True
def transformPosition(self, pos):
'''
rotates the position to the VREP and urscript version of global space
:param pos:
:return:
'''
# posRet = [pos[1],-pos[0],pos[2]]
posRet = pos
posRet = np.array(posRet)
posRet -= self.positionDisplacement
return posRet.tolist()
def transformQuat(self, quat):
'''
rotates the quaternion to the VREP and urscript version of global space
:param pos:
:return:
'''
quatMat = T.quaternion_matrix(quat)
retMat = np.zeros((4,4))
retMat[:,0] = quatMat[:,1]
retMat[:,1] = -quatMat[:,0]
retMat[:,2] = quatMat[:,2]
return T.quaternion_from_matrix(quatMat)
def getGripperValue(self):
'''
returns the gripper value (between 0 and 0.085) that corresponds to the encoder value in radians
:param enocder: encoder value in radians
:return:
'''
# 0 for open, 1 for close
flag = 0
pos = self.vars.eeGoalPos
quat = self.vars.eeGoalOr
encoder = self.vars.encoderValue
if encoder < 0.035 and flag == 0:
encoder = 0.0
flag = 1
elif encoder >= 0.035 and flag == 1:
encoder = 0.085
flag = 0
return encoder
'''
distance = self.vars.TongsTransform.getTongsDistance(pos,quat,encoder)
if distance < 0.0:
return 0.0
#if distance > 0.066675:
# return 0.085
u = distance / 0.34
return u*0.085
'''
################## DEPRECATED FUNCTIONS ##############################################
'''
# get the next position and orientation in the file
# note that the position and orientation don't have to be at the same time in the csv
# quaternion is [w,x,y,z]
def getNextHandConfig(filePtr):
DEPRECATED
get the next position and orientation in the file
note that the position and orientation don't have to be at the same time in the csv
quaternion is [w,x,y,z]
:param filePtr:
:return:
posRet = []
quatRet = []
timeRet = []
while posRet == [] or quatRet == []:
line = filePtr.readline()
if line == '':
return [posRet, quatRet, timeRet]
lineArr = line.split(',')
time = extractTime(lineArr[0])
if posRet == []:
posXs = lineArr[1]
posYs = lineArr[2]
posZs = lineArr[3]
if not posXs == '':
posRet = [float(posXs), float(posYs), float(posZs)]
if not posRet == [] and not quatRet == []:
timeRet = time
break
if quatRet == []:
quatWs = lineArr[4]
quatXs = lineArr[5]
quatYs = lineArr[6]
quatZs = lineArr[7]
if not quatWs == '':
quatRet = [float(quatWs),float(quatXs),float(quatYs),float(quatZs)]
if not posRet == [] and not quatRet == []:
timeRet = time
break
posRet = transformPosition(posRet)
quatRet = transformQuat(quatRet)
# posRet = transformToGripperPos(posRet,quatRet)
posRet = tc.getCenterPosition(posRet, quatRet, 0.5)
return [posRet, quatRet, timeRet]
'''
'''
def extractTime(timeLine):
takes in the time header from the bag file and extracts the time
:param timeLine:
:return:
timeArr = timeLine.split(':')
return float(timeArr[-1])
'''
| [
"hongyiwang@cs.wisc.edu"
] | hongyiwang@cs.wisc.edu |
263f279899002ea1dc4b932ee84321eec5688c55 | 20a017399925eb11943cd1cf299d536b65cb39ed | /Practice/function.py | 485781ac659a446c7c7182130d44078d0af5956c | [] | no_license | thakkarayush/practical-college | 46e72ea59bae8b501076f2ce0341bc9cb38a3d79 | 57f56e837bec84e7c2caacefaed06821f0496afc | refs/heads/master | 2023-04-10T02:28:40.209834 | 2021-04-23T11:31:45 | 2021-04-23T11:31:45 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 174 | py | def add(a,b):
return a+b
def sub(a,b):
return a-b
i=int(input("Enter number one:"))
j=int(input("Enter num two:"))
print("Sum is=",add(i,j))
print("Sub is=",sub(i,j)) | [
"ayushthakkar28954@gmail.com"
] | ayushthakkar28954@gmail.com |
2793bf43cb383c357d70e567eeb97526b4f54f97 | ba0e07b34def26c37ee22b9dac1714867f001fa5 | /azure-mgmt-web/azure/mgmt/web/models/network_access_control_entry.py | c63dd1cc8d4c8aec142f71ba8f7c5dea394ffc61 | [
"MIT"
] | permissive | CharaD7/azure-sdk-for-python | b11a08ac7d24a22a808a18203072b4c7bd264dfa | 9fdf0aac0cec8a15a5bb2a0ea27dd331dbfa2f5c | refs/heads/master | 2023-05-12T12:34:26.172873 | 2016-10-26T21:35:20 | 2016-10-26T21:35:20 | 72,448,760 | 1 | 0 | MIT | 2023-05-04T17:15:01 | 2016-10-31T15:14:09 | Python | UTF-8 | Python | false | false | 1,424 | py | # coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
#
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is
# regenerated.
# --------------------------------------------------------------------------
from msrest.serialization import Model
class NetworkAccessControlEntry(Model):
"""NetworkAccessControlEntry.
:param action: Possible values include: 'Permit', 'Deny'
:type action: str or :class:`AccessControlEntryAction
<azure.mgmt.web.models.AccessControlEntryAction>`
:param description:
:type description: str
:param order:
:type order: int
:param remote_subnet:
:type remote_subnet: str
"""
_attribute_map = {
'action': {'key': 'action', 'type': 'AccessControlEntryAction'},
'description': {'key': 'description', 'type': 'str'},
'order': {'key': 'order', 'type': 'int'},
'remote_subnet': {'key': 'remoteSubnet', 'type': 'str'},
}
def __init__(self, action=None, description=None, order=None, remote_subnet=None):
self.action = action
self.description = description
self.order = order
self.remote_subnet = remote_subnet
| [
"lmazuel@microsoft.com"
] | lmazuel@microsoft.com |
1cb0d8afd4196917880ccf9ba7a98c435358c40d | a398fc464a9d9648c13c1b88027e428c69788847 | /bitmap.py | 803296f273e6d7a64e7fa3e68098bacaf223683f | [
"Apache-2.0"
] | permissive | dadrian/blinken-board | 5d2e8ff0bc8a00e900e7764fc11950301a2c05fc | 994131a9dcda20b9a6af47d0349d55ce8d023ddb | refs/heads/master | 2021-03-27T18:24:46.842916 | 2015-11-07T04:02:16 | 2015-11-07T04:02:16 | 33,059,216 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,182 | py | #!/usr/bin/python
from PIL import Image
from PIL import ImageFilter
from board import Board
import colorsys
import sys
import math
import pygame
import time
im = Image.open(sys.argv[1])
#px = im.load()
pygame.init()
board = Board(host=('141.212.141.4', 1337))
WIDTH = 57
HEIGHT = 44
ratio = float(im.size[0]) / im.size[1]
print(im.size)
print('ratio: %f' % (ratio))
resize_w = WIDTH
resize_h = HEIGHT
left_offset = 0
top_offset = 0
if (ratio * HEIGHT) < WIDTH:
print('vertically limitted')
resize_w = int(HEIGHT * ratio)
resize_h = HEIGHT
left_offset = (WIDTH - resize_w) / 2
else:
print('horizontal limitted')
resize_w = WIDTH
resize_h = int(WIDTH / ratio)
top_offset = (HEIGHT - resize_h) / 2
print('resizing to (%d, %d) top: %d, left: %d' % (resize_w, resize_h, top_offset, left_offset))
#degree = 0
while True:
px = im.resize((resize_w, resize_h), Image.ANTIALIAS).load() #filter(ImageFilter.Kernel((3,3), (0, -1, 0, -1, 5, -1, 0, -1, 0))).load()
#if (degree % 90 == 0):
# degree += 0.1
#px = im.rotate(degree).load()
#degree += 15
for x in xrange(WIDTH):
for y in xrange(HEIGHT):
try:
im_x = x - left_offset
im_y = y - top_offset
if im_x < resize_w and im_y < resize_h:
r = px[im_x, im_y][0]
g = px[im_x, im_y][1]
b = px[im_x, im_y][2]
if len(px[im_x, im_y]) > 3:
a = px[im_x, im_y][3]
r *= (a/255.0)
g *= (a/255.0)
b *= (a/255.0)
else:
r, g, b = (0, 0, 0)
board.set_light(x, y, (int(r), int(g), int(b)))
except:
pass
#board.display()
board.send_board()
for event in pygame.event.get():
if event.type == pygame.QUIT:
pygame.quit();
sys.exit();
time.sleep(0.025)
while True:
for event in pygame.event.get():
if event.type == pygame.QUIT:
pygame.quit();
sys.exit();
time.sleep(0.01)
| [
"ewust@umich.edu"
] | ewust@umich.edu |
b59e4d3762439cd9cf59f8a336bd547a589a115d | 93bcf6424deb58d1a833168289acbf493c545cf7 | /Algorithms/0015_3Sum.py | e3d81e4d4dcbd30e326b22b905d6ea2ddfea8dc3 | [
"MIT"
] | permissive | drjordy66/LeetCode | 94590a1cc91f336b1bcf5c7ab11a390ff35c8702 | ba0c04ee5ddc8c9177dd2995be95dd6d0640bc38 | refs/heads/master | 2021-07-10T15:39:10.029964 | 2019-02-22T23:24:27 | 2019-02-22T23:24:27 | 146,994,193 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 973 | py | class Solution:
def threeSum(self, nums):
"""
:type nums: List[int]
:rtype: List[List[int]]
"""
nums = sorted(nums)
sol_set = []
for i in range(len(nums)):
l_index = i + 1
r_index = len(nums) - 1
while l_index < r_index:
if nums[l_index] + nums[r_index] < -nums[i]:
l_index += 1
elif nums[l_index] + nums[r_index] > -nums[i]:
r_index -= 1
else:
sol_set.append((nums[l_index], nums[r_index], nums[i]))
while l_index < r_index and nums[l_index] == nums[l_index + 1]:
l_index += 1
while l_index < r_index and nums[r_index] == nums[r_index - 1]:
r_index -= 1
l_index += 1
r_index -= 1
sol_set = list(set(sol_set))
return sol_set
| [
"noreply@github.com"
] | noreply@github.com |
f6c9d76062cde0cd981ce264be31a195120bb27f | a2531d7b363b7bf35f3f4254fa0e34722b858321 | /experiment/experiment_code/models/cifar/resnet.py | cc3a3629765d6c9e3242deafe05b6fd833bb9571 | [] | no_license | sgflower66/SPI-Optimizer | 08c7fc110ef3ff6299de5c1cc3a8f218840b5b05 | 1cece09f350bbbeb1105e209c20292dad77e1c2a | refs/heads/master | 2020-04-29T08:04:26.039221 | 2019-03-16T14:02:48 | 2019-03-16T14:02:48 | 175,973,850 | 4 | 1 | null | null | null | null | UTF-8 | Python | false | false | 4,669 | py | from __future__ import absolute_import
'''Resnet for cifar dataset.
Ported form
https://github.com/facebook/fb.resnet.torch
and
https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
(c) YANG, Wei
'''
import torch.nn as nn
import math
__all__ = ['resnet']
def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, depth, num_classes=1000):
super(ResNet, self).__init__()
# Model type specifies number of layers for CIFAR-10 model
assert (depth - 2) % 6 == 0, 'depth should be 6n+2'
n = (depth - 2) / 6
block = Bottleneck if depth >=44 else BasicBlock
self.inplanes = 16
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1,
bias=False)
self.bn1 = nn.BatchNorm2d(16)
self.relu = nn.ReLU(inplace=True)
self.layer1 = self._make_layer(block, 16, n)
self.layer2 = self._make_layer(block, 32, n, stride=2)
self.layer3 = self._make_layer(block, 64, n, stride=2)
self.avgpool = nn.AvgPool2d(8)
self.fc = nn.Linear(64 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, int(blocks)):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x) # 32x32
x = self.layer1(x) # 32x32
x = self.layer2(x) # 16x16
x = self.layer3(x) # 8x8
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
def resnet(**kwargs):
"""
Constructs a ResNet model.
"""
return ResNet(**kwargs)
| [
"sgflower66@gmail.com"
] | sgflower66@gmail.com |
fb384863ec5d9329a46985b438244bc624df9626 | c24212464eb84588edc7903a8905f2a881d578c4 | /migrations/versions/46e80c86a0fb_.py | 044aab1785b0ec919d392c0f49889f32428e6dcb | [] | no_license | the-akira/Flask-Library | c533dc2fd1ac2d3d9e2732e7c7bed5b8cc7ca4bd | 833e77660053b1e95975ccdf8bf41a035722975c | refs/heads/master | 2023-05-25T12:08:15.898134 | 2023-02-07T23:36:50 | 2023-02-07T23:36:50 | 205,951,022 | 5 | 2 | null | 2023-02-15T22:08:36 | 2019-09-02T23:26:50 | HTML | UTF-8 | Python | false | false | 1,041 | py | """empty message
Revision ID: 46e80c86a0fb
Revises: 5ef733a72780
Create Date: 2022-05-14 05:28:36.082684
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = '46e80c86a0fb'
down_revision = '5ef733a72780'
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.add_column('analysis', sa.Column('user_id', sa.Integer(), nullable=True))
op.create_foreign_key(None, 'analysis', 'user', ['user_id'], ['id'])
op.alter_column('book', 'image_book',
existing_type=sa.VARCHAR(length=20),
nullable=True)
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.alter_column('book', 'image_book',
existing_type=sa.VARCHAR(length=20),
nullable=False)
op.drop_constraint(None, 'analysis', type_='foreignkey')
op.drop_column('analysis', 'user_id')
# ### end Alembic commands ###
| [
"gabrielfelippe90@gmail.com"
] | gabrielfelippe90@gmail.com |
ebf26bf8df233f24cb2020c21a733fe1f4540e86 | 4824c0cd9ddbe6be48a179ee72093e5d3d174e9e | /user/signals.py | f872ad5c22b08f1b3b1a688c2d0c1df7edbc3dd4 | [] | no_license | SHILUXI/mysite | 3f3765670840820eb9edad5075eae63714880ca1 | 9d94c105d63df4a48c03ec47a08cde43d105009e | refs/heads/master | 2023-01-11T17:58:22.247730 | 2020-01-03T10:28:08 | 2020-01-03T10:28:08 | 229,091,742 | 0 | 0 | null | 2022-12-26T21:00:23 | 2019-12-19T16:09:36 | JavaScript | UTF-8 | Python | false | false | 477 | py | from django.db.models.signals import post_save
from django.dispatch import receiver
from notifications.signals import notify
from django.contrib.auth.models import User
from django.urls import reverse
@receiver(post_save,sender=User)
def send_notification(sender,instance,**kwargs):
if kwargs['created'] == True:
verb = '注册成功'
url = reverse('user_info')
notify.send(instance, recipient=instance, verb=verb, action_object=instance, url=url) | [
"2417565941@qq.com"
] | 2417565941@qq.com |
314851d86273e01bceb3c684924368522f40ba2e | 9a3b9de5eba5585cff302dde267920269ab338ae | /zeus/networks/quant.py | eea5ce5ab0b4276fa82f0bbd22fd4222941b3d2e | [
"MIT"
] | permissive | Jizhongpeng/xingtian | 835f5b8d997d5dcdd13a77ad10bc658704892b18 | a9bdde734f14111854ed666dfdc780d5fe5311b1 | refs/heads/master | 2023-01-09T13:07:41.498246 | 2020-11-10T01:24:42 | 2020-11-10T01:24:42 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,935 | py | # -*- coding: utf-8 -*-
# Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
# This program is free software; you can redistribute it and/or modify
# it under the terms of the MIT License.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# MIT License for more details.
"""Quantized Convlution."""
import logging
import zeus
from zeus.modules.operators import ops, quant
from zeus.common import ClassFactory, ClassType
@ClassFactory.register(ClassType.SEARCH_SPACE)
class Quantizer(object):
"""Model Quantization class."""
def __init__(self, model, nbit_w_list=8, nbit_a_list=8, skip_1st_layer=True):
super().__init__()
self.idx = 0
self.nbit_w_list = nbit_w_list
self.nbit_a_list = nbit_a_list
self.skip_1st_layer = skip_1st_layer
self.model = model
def _next_nbit(self):
"""Get next nbit."""
if isinstance(self.nbit_w_list, list) and isinstance(self.nbit_a_list, list):
nbit_w, nbit_a = self.nbit_w_list[self.idx], self.nbit_a_list[self.idx]
self.idx += 1
else:
nbit_w, nbit_a = self.nbit_w_list, self.nbit_a_list
return nbit_w, nbit_a
def _quant_conv(self, model):
"""Quantize the convolutional layer."""
if not isinstance(model, ops.Conv2d):
return model
nbit_w, nbit_a = self._next_nbit()
quant_model = quant.QuantConv(model.in_channels, model.out_channels, model.kernel_size,
model.stride, model.padding, model.dilation, model.groups, model.bias)
quant_model.build(nbit_w=nbit_w, nbit_a=nbit_a)
if zeus.is_torch_backend():
if nbit_w == 8:
quant_model = ops.QuantizeConv2d(model.in_channels, model.out_channels, model.kernel_size,
model.stride, model.padding, model.dilation, model.groups,
quant_bit=nbit_w)
return quant_model
def __call__(self):
"""Quantize the entire model."""
if self.nbit_w_list is None or self.nbit_a_list is None:
logging.warning("nbit_w or nbit_a is None, model can not be quantified.")
return self.model
is_first_conv = True
for name, layer in list(self.model.named_modules()):
if not isinstance(layer, ops.Conv2d) and self.skip_1st_layer:
continue
if is_first_conv:
is_first_conv = False
continue
quant_conv = self._quant_conv(layer)
self.model.set_module(name, quant_conv)
return self.model
def custom_hooks(self):
"""Calculate flops and params."""
return quant.quant_custom_ops()
| [
"hustqj@126.com"
] | hustqj@126.com |
8721841952456ec2d5d473bb01f9626b6c1db934 | a3eac72da2fa092a436c8a9b8a9a63672c9aa1d1 | /urlshortener/migrations/0001_initial.py | c88d32a985f7d7ab2aeb98bb6617875c2491fb34 | [] | no_license | yunisdev/django-link-shortener | 15f6eceb92c423ebeebfe5fe35f9c1c08e7c5441 | 9236082513e0c42e62cdb49a1692c38622899bf6 | refs/heads/master | 2023-03-22T11:17:09.699634 | 2021-03-21T19:21:58 | 2021-03-21T19:21:58 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 662 | py | # Generated by Django 3.1.7 on 2021-03-20 17:21
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='ShortedLink',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('original_link', models.CharField(max_length=200)),
('short_link', models.CharField(blank=True, max_length=200, null=True)),
('created_at', models.DateTimeField(auto_now_add=True)),
],
),
]
| [
"yunisdev.04@gmail.com"
] | yunisdev.04@gmail.com |
eab364b1475b8c64056308622fef756a8a15361a | 59df1401a36275544cecc10ed47599c5b63bd03c | /data/addproductform.py | 8278cb41c0903b8de96e12be1d080254630ff949 | [] | no_license | Likogeles/OnlineShop | b28896d5a0cf46ee5080f1e623ea2d5233eb7bc2 | 0828bdcb50ae5415c57ee5ac52855fb21d23dcaf | refs/heads/master | 2021-03-26T17:08:03.268045 | 2020-05-11T12:17:18 | 2020-05-11T12:17:18 | 247,724,661 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 826 | py | from flask_wtf import FlaskForm
from wtforms import StringField, PasswordField, SubmitField
from wtforms.validators import DataRequired
from flask_wtf.file import FileField, FileRequired, FileAllowed
from wtforms.fields.html5 import EmailField
class AddProductForm(FlaskForm):
title = StringField('Название', validators=[DataRequired()])
number = StringField('Количество', validators=[DataRequired()])
description = StringField('Описание', validators=[DataRequired()])
price = StringField('Цена', validators=[DataRequired()])
product_type = StringField('Тип товара', validators=[DataRequired()])
# image = FileField("Изображение", validators=[FileRequired(), FileAllowed(['jpg', 'png'], 'Images only!')])
submit = SubmitField('Добавить')
| [
"Yhhuu@yandex.ru"
] | Yhhuu@yandex.ru |
7300616012e7ac41dbf6ea787dec29101007641b | cc8404f63e83c4dfe29d9c2f2333fe02727a3d4a | /setup.py | 650e3e2ecabba01fa1f44628e9d5925312626fb5 | [
"MIT"
] | permissive | senderle/headless | 2657701d2594e68047a6bbc9e323cadb78cd21ed | a05029eafd0649e12d53e02a0bf7589ebade55bb | refs/heads/main | 2021-07-17T07:46:21.680918 | 2020-04-08T17:53:57 | 2020-04-08T17:53:57 | 216,416,385 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,811 | py | """Setup script for the headless package.
"""
# Based on the PyPA sample project here:
# https://github.com/pypa/sampleproject/
# Always prefer setuptools over distutils
from setuptools import setup, find_packages
# To use a consistent encoding
from codecs import open
from os import path
here = path.abspath(path.dirname(__file__))
# Get the long description from the README file
with open(path.join(here, 'README.md'), encoding='utf-8') as f:
long_description = f.read()
setup(
name='headless',
# Versions should comply with PEP440. For a discussion on single-sourcing
# the version across setup.py and the project code, see
# https://packaging.python.org/en/latest/single_source_version.html
version='0.0.1a',
description='Strip headers from full-text documents in HathiTrust',
long_description=long_description,
# The project's main homepage.
url='https://github.com/senderle/headless',
# Author details
author='Scott Enderle',
author_email='scott.enderle@gmail.com',
# Choose your license
license='MIT',
# See https://pypi.python.org/pypi?%3Aaction=list_classifiers
classifiers=[
# How mature is this project? Common values are
# 3 - Alpha
# 4 - Beta
# 5 - Production/Stable
'Development Status :: 3 - Alpha',
# Indicate who your project is intended for
'Intended Audience :: Developers',
'Topic :: Text Processing :: Linguistic',
# Pick your license as you wish (should match "license" above)
'License :: OSI Approved :: MIT License',
# Specify the Python versions you support here. In particular, ensure
# that you indicate whether you support Python 2, Python 3 or both.
'Programming Language :: Python :: 3',
],
# What does your project relate to?
keywords='hathitrust text',
# You can just specify the packages manually here if your project is
# simple. Or you can use find_packages().
# packages=find_packages(exclude=['contrib', 'docs', 'tests']),
packages=find_packages(),
# Alternatively, if you want to distribute just a my_module.py, uncomment
# this:
# py_modules=["my_module"],
# List run-time dependencies here. These will be installed by pip when
# your project is installed. For an analysis of "install_requires" vs pip's
# requirements files see:
# https://packaging.python.org/en/latest/requirements.html
install_requires=['editdistance'],
# List additional groups of dependencies here (e.g. development
# dependencies). You can install these using the following syntax,
# for example:
# $ pip install -e .[dev,test]
# extras_require={
# 'dev': ['check-manifest'],
# 'test': ['coverage'],
# },
# If there are data files included in your packages that need to be
# installed, specify them here. If using Python 2.6 or less, then these
# have to be included in MANIFEST.in as well.
# package_data={
# 'headless': ['_fake_data/*.json'],
# },
# Although 'package_data' is the preferred approach, in some case you may
# need to place data files outside of your packages. See:
# http://docs.python.org/3.4/distutils/setupscript.html#installing-additional-files # noqa
# In this case, 'data_file' will be installed into '<sys.prefix>/my_data'
# data_files=[('my_data', ['data/data_file'])],
# To provide executable scripts, use entry points in preference to the
# "scripts" keyword. Entry points provide cross-platform support and allow
# pip to create the appropriate form of executable for the target platform.
# entry_points={
# 'console_scripts': [
# 'headless=headless:main',
# ],
# },
)
| [
"scott.enderle@gmail.com"
] | scott.enderle@gmail.com |
1f9eec61d69f3495a1ba4af52454157e3b740193 | 1bf417659e51826db145fd178d0d88d19562e936 | /pytorch_mrc/data/batch_generator.py | a1a2213ba7dd8a13aab79fcbb02923b9d5b841e9 | [] | no_license | topDreamer/PyTorch-MRCToolkit | 48fc1f9dba009e49799e8d26a8a9f861c7aa93e7 | 23ec374338509a2e61d0060a43f2d6a32fe337d3 | refs/heads/master | 2022-02-17T17:01:39.173910 | 2019-08-09T11:36:45 | 2019-08-09T11:36:45 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 11,587 | py | import pickle
import logging
import multiprocessing
import torch
from torch.utils.data import Dataset, DataLoader
class BatchGenerator(object):
def __init__(self):
pass
def build(self, vocab, instances,
batch_size=32,
shuffle=False,
max_context_len=400,
max_question_len=50,
use_char=True,
max_word_len=30,
additional_fields=None,
feature_vocab=None,
num_parallel_calls=0):
"""
Build the batch generator, including build dataset and build dataloader
"""
self.vocab = vocab
self.instances = instances
self.batch_size = batch_size
self.shuffle = shuffle
self.max_context_len = max_context_len
self.max_question_len = max_question_len
self.use_char = use_char
self.max_word_len = max_word_len
self.additional_fields = additional_fields if additional_fields is not None else list()
self.feature_vocab = feature_vocab if feature_vocab is not None else dict()
self.num_parallel_calls = num_parallel_calls if num_parallel_calls > 0 else multiprocessing.cpu_count() // 2
if self.instances is None or len(self.instances) == 0:
raise ValueError('empty instances!!')
self.dataset = self._build_dataset_pipeline()
self.dataloader = self._build_dataloader_pipeline()
def save(self, file_path):
"""
Save the attribute of BatchGenerator
"""
logging.info("Saving BatchGenerator at {}".format(file_path))
# pickle can't save generator and dataloader, so we skip those fields
dataloader_tmp = self.dataloader
self.generator, self.dataloader = None, None
with open(file_path, "wb") as f:
pickle.dump(self.__dict__, f)
self.dataloader = dataloader_tmp
def load(self, file_path):
"""
Load the saved file and rebuilt BatchGenerator
"""
logging.info("Loading BatchGenerator at {}".format(file_path))
with open(file_path, 'rb') as f:
vocab_data = pickle.load(f)
self.__dict__.update(vocab_data)
# we don't save the value of generator and dataloader, so we build they here
self.generator = None
self.dataloader = self._build_dataloader_pipeline()
def init(self):
"""
Initialize the dataloader generator
"""
self.generator = BatchGenerator._generator(self.dataloader)
def next(self):
"""
Get next batch data of dataloader
"""
if self.generator is None:
raise Exception('you must do init before do next.')
return next(self.generator)
def get_dataset_size(self):
return len(self.dataset)
def get_batch_size(self):
return self.batch_size
def get_raw_dataset(self):
"""
When evaluating and predicting, you may need the raw dataset to generate answers
"""
return self.instances
def get_vocab(self):
return self.vocab
@staticmethod
def _generator(dataloader):
for batch_data in dataloader:
yield batch_data
@staticmethod
def _dynamic_padding(example, pad_len, pad_thing):
example = (example + [pad_thing] * (pad_len - len(example)))[:pad_len]
return example
@staticmethod
def _detect_input_type(instance, additional_fields=None):
instance_keys = instance.keys()
fields = ['context_tokens', 'question_tokens', 'answer_start', 'answer_end']
try:
for f in fields:
assert f in instance_keys
except Exception:
raise ValueError('A instance should contain at least "context_tokens", "question_tokens", \
"answer_start", "answer_end" four fields!')
if additional_fields is not None and isinstance(additional_fields, list):
fields.extend(additional_fields)
def get_type(value):
if isinstance(value, float):
return torch.float32
elif isinstance(value, int):
return torch.int64
elif isinstance(value, str):
return str
elif isinstance(value, bool):
return bool
else:
return None
input_type = {'answer_start': None, 'answer_end': None}
for field in fields:
if instance[field] is None:
if field not in ('answer_start', 'answer_end'):
logging.warning('Data type of field "%s" not detected! Skip this field.', field)
continue
elif isinstance(instance[field], list):
if len(instance[field]) == 0:
logging.warning('Data shape of field "%s" not detected! Skip this field.', field)
continue
field_type = get_type(instance[field][0])
if field_type is not None:
input_type[field] = field_type
else:
logging.warning('Data type of field "%s" not detected! Skip this field.', field)
else:
field_type = get_type(instance[field])
if field_type is not None:
input_type[field] = field_type
else:
logging.warning('Data type of field "%s" not detected! Skip this field.', field)
return input_type
def _build_dataset_pipeline(self):
# 1. Check the input-data type and filter invalid keys
input_type_dict = BatchGenerator._detect_input_type(self.instances[0], self.additional_fields)
filtered_instances = [{field: instance[field] for field in input_type_dict} for instance in self.instances]
# 2. Some preprocessing, including char extraction, lowercasing, length
def transform_new_instance(instance):
context_tokens = instance['context_tokens']
question_tokens = instance['question_tokens']
if self.use_char:
def get_seq_char_ids(word_tokens):
result = []
for word in word_tokens:
word_char_ids = [self.vocab.get_char_idx(char) for char in word]
result.append(word_char_ids)
return result
instance['context_char_ids'] = get_seq_char_ids(context_tokens)
instance['question_char_ids'] = get_seq_char_ids(question_tokens)
instance['context_word_len'] = [len(word) for word in context_tokens]
instance['question_word_len'] = [len(word) for word in question_tokens]
# if do_lowercasing, we will do it in `get_word_idx` function
instance['context_ids'] = [self.vocab.get_word_idx(token) for token in context_tokens]
instance['question_ids'] = [self.vocab.get_word_idx(token) for token in question_tokens]
instance['context_len'] = len(context_tokens)
instance['question_len'] = len(question_tokens)
# filter the str data, because we don't need them when running neural network
for field, field_type in input_type_dict.items():
if field_type == str:
del instance[field]
return instance
new_instances = [transform_new_instance(instance) for instance in filtered_instances]
return MRCDataset(new_instances)
def _build_dataloader_pipeline(self):
word_pad_idx = self.vocab.get_word_pad_idx()
if self.use_char:
char_pad_idx = self.vocab.get_char_pad_idx()
def mrc_collate(batch):
result = {}
for key in batch[0].keys():
result[key] = []
# 1. Handle the word level sequence data
# 1.1 Get batch pad length
pad_context_len = min(self.max_context_len, max([sample['context_len'] for sample in batch]))
pad_question_len = min(self.max_question_len, max([sample['question_len'] for sample in batch]))
# 1.2 Padding context and question
for sample in batch:
sample['context_ids'] = BatchGenerator._dynamic_padding(sample['context_ids'], pad_context_len, word_pad_idx)
sample['question_ids'] = BatchGenerator._dynamic_padding(sample['question_ids'], pad_question_len, word_pad_idx)
sample['context_len'] = min(sample['context_len'], pad_context_len)
sample['question_len'] = min(sample['question_len'], pad_question_len)
# 2. Handle the char level data
if self.use_char:
# 2.1 Padding sample `char ids` and `word len` to batch max length
# TODO padding with 1 length is ok ?
for sample in batch:
sample['context_char_ids'] = BatchGenerator._dynamic_padding(
sample['context_char_ids'], pad_context_len, [char_pad_idx])
sample['question_char_ids'] = BatchGenerator._dynamic_padding(
sample['question_char_ids'], pad_question_len, [char_pad_idx])
sample['context_word_len'] = BatchGenerator._dynamic_padding(
sample['context_word_len'], pad_context_len, 1)
sample['question_word_len'] = BatchGenerator._dynamic_padding(
sample['question_word_len'], pad_question_len, 1)
# 2.2 Get batch pad word length
pad_context_word_len = min(self.max_word_len, max([max(sample['context_word_len']) for sample in batch]))
pad_question_word_len = min(self.max_word_len, max([max(sample['question_word_len']) for sample in batch]))
# 2.3 Padding batch word len to pad word length
for sample in batch:
sample['context_char_ids'] = [BatchGenerator._dynamic_padding(char_ids, pad_context_word_len, char_pad_idx)
for char_ids in sample['context_char_ids']]
sample['question_char_ids'] = [BatchGenerator._dynamic_padding(char_ids, pad_question_word_len, char_pad_idx)
for char_ids in sample['question_char_ids']]
sample['context_word_len'] = [min(word_len, pad_context_word_len)
for word_len in sample['context_word_len']]
sample['question_word_len'] = [min(word_len, pad_question_word_len)
for word_len in sample['question_word_len']]
# 3. Convert batch data to `torch tensor`
for sample in batch:
for key, value in sample.items():
result[key].append(value)
for key, value in result.items():
result[key] = torch.tensor(value)
return result
return DataLoader(dataset=self.dataset, shuffle=self.shuffle,
batch_size=self.batch_size,
collate_fn=mrc_collate,
num_workers=self.num_parallel_calls)
class MRCDataset(Dataset):
def __init__(self, instances):
self.instances = instances
def __getitem__(self, idx):
return self.instances[idx]
def __len__(self):
return len(self.instances)
| [
"88629850@qq.com"
] | 88629850@qq.com |
13e604425bfe67eacff60bf986160796280d1f75 | c9fe27dd429741f2fd6d567e0aa157871fa89bed | /fork/introducer/introducer_api.py | efb7a706c223b3bdead2640e2d913a38101611bd | [
"Apache-2.0"
] | permissive | Fork-Network/fork-blockchain | 858d3aefe359a3fff547cf4464f45216b3718fa3 | 4e7c55b5787376dabacc8049eac49c0bb0bfd855 | refs/heads/main | 2023-06-23T00:28:14.607265 | 2021-07-24T02:23:22 | 2021-07-24T02:23:22 | 388,574,519 | 7 | 2 | null | null | null | null | UTF-8 | Python | false | false | 1,919 | py | from typing import Callable, Optional
from fork.introducer.introducer import Introducer
from fork.protocols.introducer_protocol import RequestPeersIntroducer, RespondPeersIntroducer
from fork.protocols.protocol_message_types import ProtocolMessageTypes
from fork.server.outbound_message import Message, make_msg
from fork.server.ws_connection import WSForkConnection
from fork.types.peer_info import TimestampedPeerInfo
from fork.util.api_decorators import api_request, peer_required
from fork.util.ints import uint64
class IntroducerAPI:
introducer: Introducer
def __init__(self, introducer) -> None:
self.introducer = introducer
def _set_state_changed_callback(self, callback: Callable):
pass
@peer_required
@api_request
async def request_peers_introducer(
self,
request: RequestPeersIntroducer,
peer: WSForkConnection,
) -> Optional[Message]:
max_peers = self.introducer.max_peers_to_send
if self.introducer.server is None or self.introducer.server.introducer_peers is None:
return None
rawpeers = self.introducer.server.introducer_peers.get_peers(
max_peers * 5, True, self.introducer.recent_peer_threshold
)
peers = []
for r_peer in rawpeers:
if r_peer.vetted <= 0:
continue
if r_peer.host == peer.peer_host and r_peer.port == peer.peer_server_port:
continue
peer_without_timestamp = TimestampedPeerInfo(
r_peer.host,
r_peer.port,
uint64(0),
)
peers.append(peer_without_timestamp)
if len(peers) >= max_peers:
break
self.introducer.log.info(f"Sending vetted {peers}")
msg = make_msg(ProtocolMessageTypes.respond_peers_introducer, RespondPeersIntroducer(peers))
return msg
| [
"bekbol17281923@outlook.com"
] | bekbol17281923@outlook.com |
b1da4dfe6877a164dc95df3174175bfdbca1d820 | a962cd3908b8e5939bd2746dad6202196cbfa97a | /src/CIH/Screen.py | 02c1369dd8f824ab79a49fcb535f71758299c991 | [] | no_license | amandapersampa/ClassificadorTartaruga | 4ab7231cd966b3939fcaea3bdff692646066772c | 6750ef551533fa8ca734df20129f1f4fd9da4d13 | refs/heads/master | 2021-01-11T20:01:04.226619 | 2017-05-22T15:32:23 | 2017-05-22T15:32:23 | 79,449,289 | 5 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,105 | py | import matplotlib.pyplot as plt
import numpy as np
def plot_confusion_matrix(target, cm, title='Confusion matrix', cmap=plt.cm.Blues, lbp='default'):
fig = plt.figure()
plt.clf()
ax = fig.add_subplot(111)
ax.set_aspect(1)
width = len(cm)
for x in range(width):
for y in range(width):
ax.annotate(str(cm[x][y]), xy=(y, x),
horizontalalignment='center',
verticalalignment='center')
plt.title(title)
plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Reds)
plt.colorbar(fraction=0.046, pad=0.04)
tick_marks = np.arange(len(target))
plt.xticks(tick_marks, target, rotation=45)
plt.yticks(tick_marks, target)
plt.ylabel(u'Classe Verdadeira', fontsize=16)
plt.xlabel(u'Classe Estimada', fontsize=16)
plt.tight_layout()
title = lbp+'-'+title +'.png'
plt.savefig(title)
plt.close(fig)
def saveImages_erro(error_file, name_test, name_pred, clf_name, mode_name):
iterate = 0
for i in range(len(error_file)):
fig = plt.figure()
plt.imshow(error_file[i])
folder = 'pred-' + name_pred[i] + '-esp-' + name_test[i]
base = '.\\CGT\\Result\\' + clf_name + '\\' + mode_name + '\\' + folder
plt.title('Especie: ' + name_test[i] + ' - Previsto: ' + name_pred[i])
file = 'Imagem-original'
title = base + '\\' + str(i) + file + '.png'
plt.savefig(title)
plt.close(fig)
print("File: " + title + " Salvo")
iterate+=1
'''
def saveImagemClassificador():
n_neighbors = 15
# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :2] # we only take the first two features. We could
# avoid this ugly slicing by using a two-dim dataset
y = iris.target
h = .02 # step size in the mesh
# Create color maps
cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])
for weights in ['uniform', 'distance']:
# we create an instance of Neighbours Classifier and fit the data.
clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)
clf.fit(X, y)
# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, x_max]x[y_min, y_max].
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.figure()
plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
# Plot also the training points
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.title("3-Class classification (k = %i, weights = '%s')"
% (n_neighbors, weights))
plt.show()
''' | [
"amandapersampa@hotmail.com"
] | amandapersampa@hotmail.com |
4a2f17632e371d7ea978f275afc73c141b918b4c | bb38d19debd5a2033ce699755f7f6b28f8ef8448 | /blog/models.py | ba29aecee44a027cbbfbbe13f4f0a69699d5c95d | [] | no_license | Bmusselman/my-first-blog | da34d9521d311dda38e07ab79834584415075d41 | 9ff4b3a7209021eadbc8acfa88c752b3e0e7744f | refs/heads/master | 2022-11-16T07:15:33.885339 | 2020-07-09T18:16:01 | 2020-07-09T18:16:01 | 278,142,914 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 691 | py | from django.db import models
from django.conf import settings
from django.utils import timezone
# class = defines object
# Post = name of model
# models.Model = means that Post is a Django Model (saved in database)
class Post(models.Model):
author = models.ForeignKey(settings.AUTH_USER_MODEL, on_delete=models.CASCADE)
title = models.CharField(max_length=200)
text = models.TextField()
created_date = models.DateTimeField(default=timezone.now)
published_date = models.DateTimeField(blank=True, null=True)
# publish method
def publish(self):
self.published_date = timezone.now()
self.save()
def __str__(self):
return self.title
| [
"brent.musselman@rem.remichel.com"
] | brent.musselman@rem.remichel.com |
b3e82c0ce2549e8185cdf4bf33cb4727e755bb19 | 6fe280f6697189d63df5fad7f5b53cfc7a117d25 | /Exploration.vs.Exploitation - Structural Design/src/RunSimulationWSGraph.py | 8f6e0dabdc8adfd0943a22a970f1bec3867137cc | [] | no_license | pobrienjhu/cens | 4126728564bd1d48725d603f71558eede1297414 | 046cde2d2caf4e14518ffb0fc45358091f012d52 | refs/heads/master | 2019-01-02T04:00:36.369736 | 2015-01-23T03:43:44 | 2015-01-23T03:43:44 | 28,733,297 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,341 | py | import numpy
import matplotlib.pyplot as plt
import networkx
from networkx.generators.community import *
import logging
import timeit
import SimulationUtils
import Simulation
import WSGraphGenerator
import time
logger = logging.getLogger('SimulationLogger')
dir = "Doctoral.Research/Exploration.vs.Exploitation - Structural Design/results/ws-graph-FangLee"
# Set the log levels
logger.setLevel(logging.INFO)
# The number of dimensions of reality
# 30 is the number from the original March experiment
m = [100] #[40,70,100,130,160]
# number of individuals in the organization
# 50 is the number from the original March experiment
n = [140] #,210,280,350,420]
# subgroup (clique) size
# not used in this one see k below for clustering
z = [7] #,14,28,70,140]
# Each node is connected to k nearest neighbors in ring topology
k =[6,8,10,14]# [2,4,6,8,10,14,28,70]
#rewiring probability
# test diversity of beliefs over time
#B = [0,0.1,0.5,1]
# test performance over B
B = [
0,0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0.09,
0.1,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18,0.19,
0.2,0.22,0.24,0.26,0.28]
#0.3,0.32,0.34,0.36,0.38,
#0.4,0.42,0.44,0.46,0.48,
#0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1]
# test subgroup sizes
#B = [0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1]
# degree of complexity
s = [5] #1,3,5,7,10]
# effectiveness of learning
# The p values represent the probability of change
# p1 effectiveness of learning (socialization)
pl = [0.3] #0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]
#probability of turnover
pt = [0] #[0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1.0]
#probability of change in each element of reality
pe = [0] #[0.1,0.4,0.7]
#interval of environmental change
T = 0
# number of different graphs to generate
graphsToRun = 1
# number of times to run the simulations
iterations = 25
recordEachRun = False
# Run the simulation for the ws graph
for graphCount in range(0,graphsToRun):
timeStamp = time.time()
useDir = dir+"/run-"+str(timeStamp)
SimulationUtils.validateDir(useDir)
for kIndex, kValue in enumerate(k):
for bIndex, bValue in enumerate(B):
graphGenerator = WSGraphGenerator.WSGraphGenerator(kValue,bValue,timeStamp,useDir)
Simulation.simulation(m,n,s,pl,pt,pe,T,iterations,graphGenerator,dir,recordEachRun)
| [
"pobrien_1@yahoo.com"
] | pobrien_1@yahoo.com |
1e39fb2c432a84387cac0b81c95666ae075d2d32 | 817542fe20b2f1c65e22f4e8b908367a8dcce62d | /cs61a-summer-2020-practice-final/solution/q2/tests/c.py | 9082c32d502f01d6fb68bfa28c41b204abe376bc | [] | no_license | shaangao/CS-61A-2020Summer | fa6710f7a8d48aed178748ecc93541884dfc3ff5 | eeb4c07adc2ed50eb60d339558de88552d2c3dc1 | refs/heads/master | 2023-07-08T12:23:19.756466 | 2020-12-08T02:50:19 | 2020-12-08T02:50:19 | 300,438,679 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 829 | py | test = {'name': 'c',
'points': 0.1,
'suites': [{'cases': [{'code': '>>> t1 = Tree(5, [Tree(2), Tree(1)])\n'
'\n'
'>>> fn_tree = make_checker_tree(t1)\n'
'\n'
'>>> t2 = Tree(5, [Tree(10), Tree(7)])\n'
'\n'
'>>> apply_tree(fn_tree, t2) #5 is a combo of '
"5, 10 is a combo of 52, 7 isn't a combo of "
'51\n'
'\n'
'>>> t2\n'
'Tree(True, [Tree(True), Tree(False)])\n'}],
'scored': True,
'setup': '>>> from q2 import *',
'type': 'doctest'}]} | [
"shan.kao@outlook.com"
] | shan.kao@outlook.com |
c8ae9c010971cafc4117295ae404d40f76f6df8e | 5b5d53dd0d1593f2c2475d493f664d34e4858ee2 | /Veri Yapilari/Queue/Queue.py | 52783b59620f924318a939e06ba93b51fc132320 | [] | no_license | yapbenzet/Modern_Cpp_WebSite | bf51a84ec6587631d1cee80354989759f200ec8e | 526d558490a8676d13330b80f34ec872d779f7d4 | refs/heads/master | 2023-04-04T17:30:17.150606 | 2023-03-22T06:34:43 | 2023-03-22T06:34:43 | 98,210,300 | 6 | 5 | null | null | null | null | UTF-8 | Python | false | false | 1,800 | py | class node:
def __init__(self, incomingData=None):
if (incomingData is not None):
self.data = incomingData;
self.prev = None;
else:
self.data = None;
self.prev = None;
class Stack:
def __init__(self, incomingSize):
self.size = incomingSize
self.rear = self.front = None
self.capacity = self.size
def queuePrinter(self, temp):
if(temp is None):
return
print(temp.data)
temp = temp.prev
self.queuePrinter(temp)
def isFull(self):
if(self.capacity == 0):
return True
else:
return False
def isEmpty(self):
if (self.capacity == self.size):
return True
else:
return False
def enqueue(self,incomingData):
if(self.isFull()):
print("Queue is Full!")
elif(self.isEmpty()):
self.front = node(incomingData)
self.rear = self.front
self.capacity = self.capacity - 1
else:
temp = node(incomingData)
self.rear.prev =temp
self.rear =temp
self.capacity = self.capacity - 1
def display(self):
self.queuePrinter(self.front)
def dequeue(self):
if(self.isEmpty()):
print("Queue is Empty!")
else:
temp = self.front
self.front = self.front.prev
self.capacity = self.capacity + 1
return temp.data
def peek(self):
return self.front.data
test = Stack(5)
for i in range(6):
test.enqueue(i+1)
test.display()
for i in range(6):
test.dequeue()
print("-*/*-/-*/-*/-*/-*/")
test.display()
| [
"noreply@github.com"
] | noreply@github.com |
0af7ed5c92a7008afe7dce8d65ae9ad39ac90809 | c9500ad778b8521aaa85cb7fe3239989efaa4799 | /plugins/greynoise/icon_greynoise/actions/get_tag_details/action.py | 891477fb89da35b5e6cce9779412d9d97fdeb6ce | [
"MIT"
] | permissive | rapid7/insightconnect-plugins | 5a6465e720f114d71b1a82fe14e42e94db104a0b | 718d15ca36c57231bb89df0aebc53d0210db400c | refs/heads/master | 2023-09-01T09:21:27.143980 | 2023-08-31T10:25:36 | 2023-08-31T10:25:36 | 190,435,635 | 61 | 60 | MIT | 2023-09-14T08:47:37 | 2019-06-05T17:05:12 | Python | UTF-8 | Python | false | false | 1,087 | py | import insightconnect_plugin_runtime
from .schema import GetTagDetailsInput, GetTagDetailsOutput, Input, Component
# Custom imports below
from icon_greynoise.util.util import GNRequestFailure
from greynoise.exceptions import RequestFailure
class GetTagDetails(insightconnect_plugin_runtime.Action):
def __init__(self):
super(self.__class__, self).__init__(
name="get_tag_details",
description=Component.DESCRIPTION,
input=GetTagDetailsInput(),
output=GetTagDetailsOutput(),
)
def run(self, params={}):
tag_name = params.get(Input.TAG_NAME).lower()
output = {}
try:
resp = self.connection.gn_client.metadata()
for tag in resp["metadata"]:
if tag["name"].lower() == tag_name:
output = tag
except RequestFailure as e:
raise GNRequestFailure(e.args[0], e.args[1])
if output:
return output
else:
return {"name": params.get(Input.TAG_NAME), "description": "Tag Not Found"}
| [
"noreply@github.com"
] | noreply@github.com |
45cd2ba3be8fcccb42e3efabb16acb0756d1ece6 | 42a2439b783b74da9b31f0ff64c8c32fb7a626ba | /core/.bin/generate-wallpaper | a05a9c83917b05d434c129d4d9ed0fabf1354abb | [] | no_license | zweifisch/dotfiles | 7a8401faf9adda58eaad59aa396ca36ee8167fbc | 0760f07d7651707d5348580cfc599c3a2d12a934 | refs/heads/master | 2023-06-21T04:30:28.458086 | 2023-06-11T05:34:12 | 2023-06-11T05:36:15 | 7,055,187 | 11 | 2 | null | null | null | null | UTF-8 | Python | false | false | 355 | #!/usr/bin/env python
import Image,os
from random import randint
w,h=20,10
img=Image.new('RGB',(w,h))
def get_gray(l,h):
gray = randint(l,h)
return (gray<<16)+(gray<<8)+gray
for x in range(w):
for y in range(h):
img.putpixel((x,y),get_gray(0x22,0x33))
img.save(os.path.expanduser('~/.wallpaper.png'))
os.system('feh --bg-tile ~/.wallpaper.png')
| [
"zf.pascal@gmail.com"
] | zf.pascal@gmail.com | |
f4b3ccfea408c60916b0ac624ecc49a07b5ec528 | 0bea4fe49b6cf7b5963847f597a20cf03dfad8c4 | /Codigo/ej_03_par_impar.py | 95de4bf4c872f524fa168ebfcaa78903de9d1641 | [] | no_license | ElianEstrada/Ejemplos_GrupoC | 00e9f8e9bf57544ab3e08a6252cba465da478dbe | b886e6fe006eb4c0389d53e5cd6b25ecd16c2642 | refs/heads/master | 2023-06-26T06:14:23.695857 | 2021-07-16T04:02:10 | 2021-07-16T04:02:10 | 381,535,806 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 161 | py |
numero = int(input("Ingrese un número: "))
resultado = numero % 2
if (resultado == 0):
print("El número es par")
else:
print("El número es impar") | [
"chictibiris@gmail.com"
] | chictibiris@gmail.com |
91a852d75e9889ff247c2e49d68a5c29baaa6db2 | 8bc2db1ce4ab8d733ef2f7a6e0eb2ac6e59c7e46 | /KLSadd_insertion/test/test_chap1_placer.py | 90aacfd58f9c3f13f03c3aff04b385b530aab39b | [] | no_license | edward-bian/DRMF-Seeding-Project | 184e3022f11d1f870de3cb1dc52ba40929743a8a | 5b55f1ba4ed623ca7414575902e2a5904f27a182 | refs/heads/master | 2021-01-24T05:16:34.533264 | 2017-06-09T21:56:54 | 2017-06-09T21:56:54 | 55,001,404 | 0 | 0 | null | 2016-03-29T18:43:47 | 2016-03-29T18:43:47 | null | UTF-8 | Python | false | false | 736 | py |
__author__ = 'Edward Bian'
__status__ = 'Development'
from unittest import TestCase
from updateChapters import chap_1_placer
class TestChap1Placer(TestCase):
def test_chap1_placer(self):
self.assertEquals(chap_1_placer(['WordsAndSomeMoreWords', 'SampleEquation', '\\end{document}']
, ['\\subsection*{Generalities}', '\\paragraph{MathFunction}', 'MathEquations', 'WordsAndStuff',
'\\subsection*{9.1 Wilson}', '\\paragraph{Symmetry}', 'WordsAndStuff'], [0,1,4,5])
, 'WordsAndSomeMoreWordsSampleEquation\\paragraph{\\bf KLS Addendum: Generalities}\\paragraph{MathFunction}MathEquationsWordsAndStuff\\end{document}')
| [
"eb092012@gmail.com"
] | eb092012@gmail.com |
852d34ad760800c05120ae0f5e9fd9c4878b3cb8 | 6f263522b62363a4b0284bf60fda3dd2259b6cbe | /src/ann_regression/ann_python/ann_server.py | 40cdfe8644340d0b8c6b28050f7bae9ec73b8677 | [
"BSD-3-Clause",
"LicenseRef-scancode-unknown-license-reference",
"BSD-2-Clause"
] | permissive | djmartingale/AI-mag | 38d1a91d34bdcd4c8e70a075e51afd2b86d1f72e | 9d81fe2e5b2803635f0a7168ecd50f5d800cf8e7 | refs/heads/master | 2023-02-25T15:39:07.215956 | 2021-02-01T19:06:36 | 2021-02-01T19:06:36 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 6,832 | py | # (c) 2019-2020, ETH Zurich, Power Electronic Systems Laboratory, T. Guillod
import numpy as np
import tensorflow.keras as keras
from .ann_engine import ann_run
from .ann_engine import ann_dump
from .mat_py_bridge import server
class AnnHandler(server.HandlerAbstract):
"""Server handler for ANN with Keras/TensorFlow.
Implementation of the abtract class "server.HandlerAbstract".
The handler is used by "server.PythonMatlabConnection".
The handler responds to server requests for training and evaluating ANNs.
"""
def __init__(self, fct_model, fct_train):
"""Constructor.
Parameters:
fct_model (fct): Function for creating the ANN
fct_train (fct): Function for training the ANN
"""
# init superclass
super().__init__()
# assign ANN functions
self.fct_model = fct_model
self.fct_train = fct_train
# dict containing the ANNs
self.ann_data = {}
def run_data(self, data_inp):
"""Respond to a server request.
Load, unload, train, and evaluate ANNs.
This function also manage the error handling.
Parameters:
data_inp (dict): Server request
Returns:
dict: Request response
"""
try:
print(' type: %s / n_model: %d' % (data_inp['type'], len(self.ann_data)))
data_info = self.__run_data_sub(data_inp)
data_status = {'status': np.array(True, dtype='bool')}
print(' status: ok / n_model: %d' % len(self.ann_data))
except Exception as e:
data_info = {}
data_status = {'status': np.array(False, dtype='bool')}
print(' status: fail / n_model: %d' % len(self.ann_data))
print(' exception: %s' % str(e))
data_out = {**data_info, **data_status}
return data_out
def __run_data_sub(self, data_inp):
"""Respond to a server request.
Load, unload, train, and evaluate ANNs.
Check which command is concerned and parse the corresponding data.
Parameters:
data_inp (dict): Server request
Returns:
dict: Request response
"""
if data_inp['type']=='train':
inp = data_inp['inp']
out = data_inp['out']
tag_train = data_inp['tag_train']
(model_dump, history_dump) = self.__train(tag_train, inp, out)
return {'model': model_dump, 'history': history_dump}
elif data_inp['type']=='unload':
name = data_inp['name']
self.__unload(name)
return {}
elif data_inp['type']=='load':
name = data_inp['name']
model = data_inp['model']
history = data_inp['history']
self.__load(name, model, history)
return {}
elif data_inp['type'] == 'predict':
name = data_inp['name']
inp = data_inp['inp']
out = self.__predict(name, inp)
return {'out': out}
else:
raise ValueError('invalid request type')
def __train(self, tag_train, inp, out):
"""Train an ANN and serialize the resulting model.
Parameters:
tag_train (various): Tag for enabling different training modes
inp (matrix): Matrix with the input data
out (matrix): Matrix with the output data
Returns:
bytes: Keras/TensorFlow model (serialized)
bytes: Keras/TensorFlow training history (serialized)
"""
# set tag_train for the provided function
fct_model_tmp = lambda n_sol, n_inp, n_out: self.fct_model(tag_train, n_sol, n_inp, n_out)
fct_train_tmp = lambda model, inp_ref, out_ref: self.fct_train(tag_train, model, inp_ref, out_ref)
# get the model and train it
(model, history) = ann_run.train(inp, out, fct_model_tmp, fct_train_tmp)
history = ann_dump.parse_keras_history(history)
assert self.__check_model_history(model, history), 'invalid model/history type'
# serialize the data
model_dump = ann_dump.dump_keras_model(model)
history_dump = ann_dump.dump_keras_history(history)
return (model_dump, history_dump)
def __unload(self, name):
"""Remove an ANN from the memory.
Parameters:
name (str): Name of the ANN to be removed
"""
# remove the entry (also if not existing)
self.ann_data.pop(name, None)
def __load(self, name, model_dump, history_dump):
"""Deserialize an ANN and load it to the memory.
Parameters:
name (str): Name of the ANN to be loaded
model_dump (bytes): Keras/TensorFlow model (serialized)
history_dump (bytes): Keras/TensorFlow training history (serialized)
"""
# deserialize the data
model = ann_dump.undump_keras_model(model_dump)
history = ann_dump.undump_keras_history(history_dump)
assert self.__check_model_history(model, history), 'invalid model/history type'
# load the data to the memory
self.ann_data[name] = {'model': model, 'history': history}
def __predict(self, name, inp):
"""Evaluate an ANN with given input data.
Parameters:
name (str): Name of the ANN to be evaluated
inp (matrix): Matrix with the input data
Parameters:
matrix: Matrix with the output data
"""
# get the model
model = self.ann_data[name]['model']
history = self.ann_data[name]['history']
assert self.__check_model_history(model, history), 'invalid model/history type'
# evaluate the model
out = ann_run.predict(model, inp)
return out
def __check_model_history(self, model, history):
"""Check the type of the model and training history.
Parameters:
model (model): Keras/TensorFlow model
history (dict): Keras/TensorFlow training history
Returns:
bool: Result of the check
"""
is_ok = True
is_ok = is_ok and isinstance(model, keras.Sequential)
is_ok = is_ok and isinstance(history, dict)
return is_ok
def run(hostname, port, n_connection, fct_model, fct_train):
"""Start the ANN server for MATLAB.
Parameters:
hostname (str): Server hostname
port (int): Server port
n_connection (int): Number of connection to accept
fct_model (fct): Function for creating the ANN
fct_train (fct): Function for training the ANN
"""
# lamdba to init the "ann_server.AnnHandler class"
handler_class = lambda: AnnHandler(fct_model, fct_train)
# run the server
obj = server.PythonMatlabServer(hostname, port, n_connection, handler_class)
obj.start_server()
| [
"guillod@otvam.ch"
] | guillod@otvam.ch |
95cd73267f33e76aafb272c6fc36dd88e3d19ac5 | ba21ab01b523e111a8ec4322a8faae56d48a4fc0 | /TestThread2.py | d0eb1c3a8001dfd5d06907e81da70ef8748448a6 | [] | no_license | michaelsoft/PythonTest | d8106bf04d366fb28489d744bb9d0f322680cf02 | 9e871e9dfaf02ad628542e2a53c99415487b20c5 | refs/heads/master | 2021-09-09T04:57:58.404717 | 2021-09-07T02:30:54 | 2021-09-07T02:30:54 | 139,562,227 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,189 | py | from threading import Thread, Lock
from time import time, sleep
class Account(object):
def __init__(self):
self._balance = 0
self._lock = Lock()
def deposit(self, amount):
self._lock.acquire()
try:
b = self._balance + amount
sleep(1)
self._balance = b
finally:
self._lock.release()
@property
def balance(self):
return self._balance
class DepositThread(Thread):
def __init__(self, thread_name, account, amount):
super().__init__()
self._thread_name = thread_name
self._account = account
self._amount = amount
def run(self):
print(f"{self._thread_name} - Befor - {self._account.balance}\r\n")
self._account.deposit(self._amount)
print(f"{self._thread_name} - After - {self._account.balance}\r\n")
def main():
account = Account()
threads = []
for i in range(1, 11):
t = DepositThread(f"t{i}", account, 1)
threads.append(t)
t.start()
for t in threads:
t.join()
print(account.balance)
if __name__ == '__main__':
main() | [
"michaellyk@qq.com"
] | michaellyk@qq.com |
43fa44dbaf715a5780475b488c679fe1616db94b | 1e18bf753b51b49843a212501a0f8b8f20339d24 | /blog/website/migrations/0003_post_approved.py | dc74bb21db94b31f31013e2213e811eea5e863b9 | [] | no_license | felipesavaris/curso_django3 | c68f3aca5cc671168daa46ee2608e8daf29ec855 | 977cebf837ba6ec784427f99adeba23eb8eb799b | refs/heads/master | 2022-10-01T23:47:33.467020 | 2020-06-11T00:21:40 | 2020-06-11T00:21:40 | 263,476,950 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 383 | py | # Generated by Django 3.0.6 on 2020-05-24 17:58
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('website', '0002_post_categories'),
]
operations = [
migrations.AddField(
model_name='post',
name='approved',
field=models.BooleanField(default=True),
),
]
| [
"fesavaris@gmail.com"
] | fesavaris@gmail.com |
ece96a8ab1a4a8b4a01d7421f38cbb08ca3bb3f6 | 768ef4658732bcb0af8fdc8dfb1a6a27db5ac2fe | /apps/movies/api/filters.py | bb27404aa926c969f17aab0f848ab825d552cb9c | [] | no_license | Nellyth/Movies | 0566b39fd0d3174571ad3fec9418612598ee4da3 | c365a2118b2aa621bc174cc3571b418aab14ef32 | refs/heads/master | 2022-11-30T01:08:17.207144 | 2019-06-28T21:57:43 | 2019-06-28T21:57:43 | 190,658,081 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 629 | py | import django_filters
from django_filters.rest_framework import FilterSet
from apps.movies.choices import movie_genre
class MovieFilter(django_filters.FilterSet):
title = django_filters.CharFilter(field_name='title', lookup_expr='icontains')
year = django_filters.NumberFilter(field_name='release_date', lookup_expr='year')
# genre = django_filters.MultipleChoiceFilter(field_name='genre', lookup_expr='icontains', choices=movie_genre,
# conjoined=True)
genre = django_filters.MultipleChoiceFilter(field_name='genre', lookup_expr='icontains', choices=movie_genre)
| [
"narroyo@lsv-tech.com"
] | narroyo@lsv-tech.com |
3d6fa5d921c0955e79337263686388cbffbec741 | 0a33367a0196fca52cad8f217a9443440f7179f6 | /tests/mqttcom_tests.py | 2bda9862c3685049d012f261fa16aca0cc0b5ac6 | [] | no_license | trieb/worx-landroid | 05c2e6188476cc5656767d840b53eb8f0304b585 | d518450108896d68985f1880cdb6b80b53af4b5f | refs/heads/master | 2021-06-09T11:06:55.768667 | 2021-05-31T11:20:43 | 2021-05-31T11:20:43 | 37,873,122 | 8 | 1 | null | null | null | null | UTF-8 | Python | false | false | 509 | py | import unittest
try:
import ConfigParser as ConfigParser
except:
import configparser as ConfigParser
from MqttCom import MqttCom
class TestMqttComClass(unittest.TestCase):
def setUp(self):
self.mqttc = MqttCom('trieb.asuscomm.com', 1883, True)
def test_publish(self):
'''Publish message'''
self.mqttc.publish("testing/from/office", "tjo")
def tearDown(self):
self.mqttc.loop_stop()
if __name__ == '__main__':
unittest.main()
| [
"mikael@trieb.se"
] | mikael@trieb.se |
97abd40242bc4e0f505170826179401f749ff4c9 | 86458257ae2127488fd8156b5648429f185de229 | /utils/.ipynb_checkpoints/triangulation_utils-checkpoint.py | 71b14edc52a09c11216ee1d95c95442a78a8e10b | [] | no_license | minyoungpark1/dlc_post-processing_code | eddf7430985ef8bf43023f08005ea5e87e102168 | 37ee2c7d98dd584b1d235a5e096cda36674ed250 | refs/heads/main | 2023-06-12T12:14:52.515803 | 2021-07-01T09:03:50 | 2021-07-01T09:03:50 | 366,619,786 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 6,574 | py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Dec 10 11:20:11 2019
@author: minyoungpark
"""
import os
import numpy as np
import pandas as pd
from tqdm import trange
from numpy import array as arr
def read_single_2d_data(data_path, offset, bp_interested, model_type):
# data = pd.read_csv(data_path, header=[1,2], index_col=0)
if model_type is 'dlc':
data = pd.read_csv(data_path, header=[2,3], index_col=0)
elif model_type is 'hrnet':
data = pd.read_csv(data_path, header=[0,1], index_col=0)
length = len(data.index)
index = arr(data.index)
coords = np.zeros((length, len(bp_interested), 2))
scores = np.zeros((length, len(bp_interested)))
for bp_idx, bp in enumerate(bp_interested):
bp_coords = arr(data[bp])
coords[index, bp_idx, :] = bp_coords[:, :2] + [offset[0], offset[1]]
scores[index, bp_idx] = bp_coords[:, 2]
return {'length': length,
'coords': coords,
'scores': scores}
def load_offsets_dict(config, vid_indices):
offsets_dict = dict()
for vid_idx in vid_indices:
# if record_dict is None:
if 'cameras' not in config or vid_idx not in config['cameras']:
offsets_dict[vid_idx] = [0, 0]
else:
offsets_dict[vid_idx] = config['cameras'][vid_idx]['offset']
# else:
# offsets_dict[cname] = record_dict['cameras'][cname]['video']['ROIPosition']
return offsets_dict
def load_2d_data(config, vid_indices, bp_interested, model_type):
paths_to_2d_data = config['paths_to_2d_data']
offsets_dict = load_offsets_dict(config, vid_indices)
# TODO: If there is any frame dropping, do interpolation. Now, just assume
# that there isn't any.
all_points_raw = []
all_scores = []
# all_points_raw = np.zeros((length, len(cam_names), len(bodyparts), 2))
# all_scores = np.zeros((length, len(cam_names), len(bodyparts)))
for ix_cam, (vid_idx, data_path) in \
enumerate(zip(vid_indices, paths_to_2d_data)):
out = read_single_2d_data(data_path, offsets_dict[vid_idx], bp_interested, model_type)
all_points_raw.append(out['coords'])
all_scores.append(out['scores'])
all_points_raw = np.stack(all_points_raw, axis=1)
all_scores = np.stack(all_scores, axis=1)
return {'points': all_points_raw,
'scores': all_scores}
def read_single_labeled_2d_data(data_path, bp_interested, offset):
data = pd.read_csv(data_path, header=[1,2], index_col=0)
length = len(data.index)
indices = arr(data.index)
for i, index in enumerate(indices):
indices[i] = index.split('/')[-1]
coords = np.zeros((length, len(bp_interested), 2))
for bp_idx, bp in enumerate(bp_interested):
bp_coords = arr(data[bp])
coords[:, bp_idx, :] = bp_coords[:, :] + [offset[0], offset[1]]
return {'length': length,
'coords': coords,
'indices': indices
}
def load_labeled_2d_data(config, vid_indices, bp_interested):
paths_to_2d_data = config['paths_to_labeled_2d_data']
offsets_dict = load_offsets_dict(config, vid_indices)
# TODO: If there is any frame dropping, do interpolation. Now, just assume
# that there isn't any.
all_points_raw = []
all_indices = []
all_lengths = []
# all_points_raw = np.zeros((length, len(cam_names), len(bodyparts), 2))
# all_scores = np.zeros((length, len(cam_names), len(bodyparts)))
for ix_cam, (vid_idx, data_path) in \
enumerate(zip(vid_indices, paths_to_2d_data)):
out = read_single_labeled_2d_data(data_path, bp_interested, offsets_dict[vid_idx])
all_points_raw.append(out['coords'])
all_indices.append(out['indices'])
all_lengths.append(out['length'])
min_len = min(all_lengths)
# amin_len = amin(all_lengths)
for i in range(len(all_lengths)):
all_points_raw[i] = all_points_raw[i][:min_len]
# for j in :
# if
all_points_raw = np.stack(all_points_raw, axis=1)
return {'points': all_points_raw,
'indices': all_indices}
def add_static_points(config, static, snapshots):
data_paths = config['paths_to_2d_data']
path_to_save = config['path_to_save_static_data']
if not os.path.exists(path_to_save):
print('Path to save does not exist.')
folder_input = input('Do you want to create this path (folder)? (y/n) ')
if folder_input is 'y':
os.mkdir(path_to_save)
elif folder_input is 'n':
return
else:
print('Wrong input.')
return
labels = static.keys()
for i, (snapshot, data_path) in enumerate(zip(snapshots, data_paths)):
data = pd.read_csv(data_path, header=[0,1,2], index_col=0)
for label in labels:
if np.isnan(static[label][i][0]):
x = np.zeros(len(data))
y = np.zeros(len(data))
likelihood = np.zeros(len(data))
else:
x = np.ones(len(data)) * static[label][i][0]
y = np.ones(len(data)) * static[label][i][1]
likelihood = np.ones(len(data))
# data = data.join(pd.DataFrame(x,
# columns=pd.MultiIndex.from_product([[snapshot],[label],['x']])))
# data = data.join(pd.DataFrame(y,
# columns=pd.MultiIndex.from_product([[snapshot],[label],['y']])))
# data = data.join(pd.DataFrame(likelihood,
# columns=pd.MultiIndex.from_product([[snapshot],[label],['likelihood']])))
data = data.join(pd.DataFrame(x,
columns=pd.MultiIndex.from_product([[snapshot],[label],['x']]),
index=data.index))
data = data.join(pd.DataFrame(y,
columns=pd.MultiIndex.from_product([[snapshot],[label],['y']]),
index=data.index))
data = data.join(pd.DataFrame(likelihood,
columns=pd.MultiIndex.from_product([[snapshot],[label],['likelihood']]),
index=data.index))
data.to_csv(os.path.join(path_to_save, 'cam_' + str(i) +'.csv'), mode='w') | [
"minyoungpark1@u.northwestern.edu"
] | minyoungpark1@u.northwestern.edu |
8a40f8e2b15ecb9ec5c11fb0b9088751db83d806 | 733149ead6bec27c9a696e37159b19f5cdf75498 | /random_news_browser.py | 9c0d089f9e615254755ed66c32414c1e2404ab9f | [] | no_license | Bratapfel2000/Python_RandomTools | 2ad2cf08d69d843c270ad28879365b6c6cf727fa | 8e9cbf93ba86ab65dfb95f26bc901d6f1bbaf525 | refs/heads/master | 2021-11-28T10:48:44.965086 | 2021-11-09T08:58:42 | 2021-11-09T08:58:42 | 152,756,868 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 550 | py | """
created with python 3.7
"""
import random
import webbrowser
link_list = """text file with list of links"""
"""creates a list out of text file"""
def linkliste():
fin = open(link_list)
liste = []
for line in fin:
word = line.strip()
liste.append(word)
return liste
"""takes link list and choses n random pages to open in browser"""
def random_opener(n):
liste = linkliste()
y = random.sample(liste,n)
for i in y:
webbrowser.open_new_tab(i)
random_opener(5)
| [
"noreply@github.com"
] | noreply@github.com |
2db06d443b7fadfd5bf1b848b96ecd5dfcfcd003 | 10d98fecb882d4c84595364f715f4e8b8309a66f | /linear_dynamical_systems/iterated_regression.py | 86e5d1169c94d3a0da489858d906ac228f583206 | [
"Apache-2.0",
"CC-BY-4.0"
] | permissive | afcarl/google-research | 51c7b70d176c0d70a5ee31ea1d87590f3d6c6f42 | 320a49f768cea27200044c0d12f394aa6c795feb | refs/heads/master | 2021-12-02T18:36:03.760434 | 2021-09-30T20:59:01 | 2021-09-30T21:07:02 | 156,725,548 | 1 | 0 | Apache-2.0 | 2018-11-08T15:13:53 | 2018-11-08T15:13:52 | null | UTF-8 | Python | false | false | 5,392 | py | # coding=utf-8
# Copyright 2021 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Regularized iterated regression for estimating AR parameters in ARMA models."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from statsmodels.regression.linear_model import OLS
from statsmodels.tools.tools import add_constant
from statsmodels.tsa.tsatools import lagmat
def fit_arparams_iter(outputs, inputs, p, q, r, l2_reg=0.0):
"""Iterative regression for estimating AR params in ARMAX(p, q, r) model.
The iterative AR regression process provides consistent estimates for the
AR parameters of an ARMAX(p, q, r) model after q iterative steps.
It first fits an ARMAX(p, 0, r) model with least squares regression, then
ARMAX(p, 1, r), and so on, ..., til ARMAX(p, q, r). At the i-th step, it
fits an ARMAX(p, i, r) model, according to estimated error terms from the
previous step.
For description of the iterative regression method, see Section 2 of
`Consistent Estimates of Autoregressive Parameters and Extended Sample
Autocorrelation Function for Stationary and Nonstationary ARMA Models` at
https://www.jstor.org/stable/2288340.
The implementation here is a generalization of the method mentioned in the
paper. We adapt the method for multidimensional outputs, exogenous inputs, nan
handling, and also add regularization on the MA parameters.
Args:
outputs: Array with the output values from the LDS, nans allowed.
inputs: Array with exogenous inputs values, nans allowed. Could be None.
p: AR order, i.e. max lag of the autoregressive part.
q: MA order, i.e. max lag of the error terms.
r: Max lag of the exogenous inputs.
l2_reg: L2 regularization coefficient, to be applied on MA coefficients.
Returns:
Fitted AR coefficients.
"""
if outputs.shape[1] > 1:
# If there are multiple output dimensions, fit autoregressive params on
# each dimension separately and average.
params_list = [
fit_arparams_iter(outputs[:, j:j+1], inputs, p, q, r, l2_reg=l2_reg) \
for j in xrange(outputs.shape[1])]
return np.mean(
np.concatenate([a.reshape(1, -1) for a in params_list]), axis=0)
# We include a constant term in regression.
k_const = 1
# Input dim. If inputs is None, then in_dim = 0.
in_dim = 0
if inputs is not None:
in_dim = inputs.shape[1]
# Lag the inputs to obtain [?, r], column j means series x_{t-j}.
# Use trim to drop rows with unknown values both at beginning and end.
lagged_in = np.concatenate(
[lagmat(inputs[:, i], maxlag=r, trim='both') for i in xrange(in_dim)],
axis=1)
# Since we trim in beginning, the offset is r.
lagged_in_offset = r
# Lag the series itself to p-th order.
lagged_out = lagmat(outputs, maxlag=p, trim='both')
lagged_out_offset = p
y = outputs
y_offset = 0
# Estimated residuals, initialized to 0.
res = np.zeros_like(outputs)
for i in xrange(q + 1):
# Lag the residuals to i-th order in i-th iteration.
lagged_res = lagmat(res, maxlag=i, trim='both')
lagged_res_offset = y_offset + i
# Compute offset in regression, since lagged_in, lagged_out, and lagged_res
# have different offsets. Align them.
if inputs is None:
y_offset = max(lagged_out_offset, lagged_res_offset)
else:
y_offset = max(lagged_out_offset, lagged_res_offset, lagged_in_offset)
y = outputs[y_offset:, :]
# Concatenate all variables in regression.
x = np.concatenate([
lagged_out[y_offset - lagged_out_offset:, :],
lagged_res[y_offset - lagged_res_offset:, :]
],
axis=1)
if inputs is not None:
x = np.concatenate([lagged_in[y_offset - lagged_in_offset:, :], x],
axis=1)
# Add constant term as the first variable.
x = add_constant(x, prepend=True)
if x.shape[1] < k_const + in_dim * r + p + i:
raise ValueError('Insufficient sequence length for model fitting.')
# Drop rows with nans.
arr = np.concatenate([y, x], axis=1)
arr = arr[~np.isnan(arr).any(axis=1)]
y_dropped_na = arr[:, 0:1]
x_dropped_na = arr[:, 1:]
# Only regularize the MA part.
alpha = np.concatenate(
[np.zeros(k_const + in_dim * r + p), l2_reg * np.ones(i)], axis=0)
# When L1_wt = 0, it's ridge regression.
olsfit = OLS(y_dropped_na, x_dropped_na).fit_regularized(
alpha=alpha, L1_wt=0.0)
# Update estimated residuals.
res = y - np.matmul(x, olsfit.params.reshape(-1, 1))
if len(olsfit.params) != k_const + in_dim * r + p + q:
raise ValueError('Expected param len %d, got %d.' %
(k_const + in_dim * r + p + q, len(olsfit.params)))
if q == 0:
return olsfit.params[-p:]
return olsfit.params[-(p + q):-q]
| [
"copybara-worker@google.com"
] | copybara-worker@google.com |
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